Denis Martinez

Anthony Legrand

Julien Gronnier

Marion Decossas

Paul Gouguet

Olivier Lambert

Melanie Berbon

Loris Verron

Axelle Grelard

Julien Gronnier B#

Mélanie Berbon

Axelle Grélard

Veronique Germain

Antoine Loquet

Sébastien Mongrand
email: sebastien.mongrand@u-bordeaux.fr

Birgit Habenstein
email: b.habenstein@iecb.u-bordeaux.fr

Coiled-coil oligomerization controls localization of the plasma membrane REMORINs

Keywords:

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Membrane nanodomains are dynamic cellular signalling platforms in the plasma membrane of a size of up to a several hundred nanometers that execute essential messaging events between cells and their surroundings [START_REF] Lingwood | Lipid rafts as a membrane-organizing principle[END_REF]. Selective dynamic recruitment of specific proteins mediates the formation of functional membrane sub-compartments, related to signal transduction [START_REF] Grecco | Signaling from the living plasma membrane[END_REF] and membrane trafficking [START_REF] Simons | Membrane organization and lipid rafts[END_REF]. Clustering of proteins into membrane compartments is notably driven by protein-lipid and protein-protein interactions [START_REF] Lingwood | Lipid rafts as a membrane-organizing principle[END_REF]. A significant number of membrane proteins are involved in protein-protein interactions leading to homo-or heterooligomers [START_REF] Mcbride | Global analysis of membrane-associated protein oligomerization using protein correlation profiling[END_REF]. Protein homo-or hetero-oligomerization has been proposed to mediate protein clustering [START_REF] Kasai | Single-molecule imaging revealed dynamic GPCR dimerization[END_REF][START_REF] Suzuki | Transient GPI-anchored protein homodimers are units for raft organization and function[END_REF]. Moreover, specific nanoclustering of proteins can drive functional compartmentalization, i.e. distinct nanodomains can be enriched in distinct nanodomain-associated proteins [START_REF] Bucherl | Plant immune and growth receptors share common signalling components but localise to distinct plasma membrane nanodomains[END_REF][START_REF] Jarsch | Plasma Membranes Are Subcompartmentalized into a Plethora of Coexisting and Diverse Microdomains in Arabidopsis and Nicotiana benthamiana[END_REF]. As a consequence of the complexity of the system, i.e. proteins associated to membrane domains, insights into the structural and mechanistic organization of these proteins are sparse.

The REMORIN family consists of six phylogenetically distinguishable groups [START_REF] Raffaele | Genome-wide annotation of remorins, a plant-specific protein family: evolutionary and functional perspectives[END_REF] of plant-specific nanodomain-organized proteins of the innerleaflet of the plasma membrane (PM). Members of the REMORIN family carry out multiple functions related to biotic and abiotic stimuli [START_REF] Jarsch | Perspectives on remorin proteins, membrane rafts, and their role during plant-microbe interactions[END_REF][START_REF] Lefebvre | A remorin protein interacts with symbiotic receptors and regulates bacterial infection[END_REF][START_REF] Raffaele | Remorin, a solanaceae protein resident in membrane rafts and plasmodesmata, impairs potato virus X movement[END_REF][START_REF] Gui | Grain setting defect1, encoding a remorin protein, affects the grain setting in rice through regulating plasmodesmatal conductance[END_REF]. In contrast to a highly variable Nterminal region, putatively implicated in protein-protein interactions [START_REF] Marin | The intrinsically disordered N-terminal region of AtREM1.3 remorin protein mediates protein-protein interactions[END_REF][START_REF] Toth | Functional domain analysis of the Remorin protein LjSYMREM1 in Lotus japonicus[END_REF], all REMORINs contain a conserved C-terminal region that encompasses a predicted coiled-coil domain [START_REF] Bariola | Remorins form a novel family of coiled coil-forming oligomeric and filamentous proteins associated with apical, vascular and embryonic tissues in plants[END_REF][START_REF] Raffaele | Genome-wide annotation of remorins, a plant-specific protein family: evolutionary and functional perspectives[END_REF][START_REF] Reymond | Cloning of a cDNA encoding a plasma membrane-associated, uronide binding phosphoprotein with physical properties similar to viral movement proteins[END_REF], as well as a short membrane anchor domain [START_REF] Gronnier | Structural basis for plant plasma membrane protein dynamics and organization into functional nanodomains[END_REF][START_REF] Perraki | Plasma membrane localization of Solanum tuberosum remorin from group 1, homolog 3 is mediated by conformational changes in a novel C-terminal anchor and required for the restriction of potato virus X movement[END_REF].

Coiled-coil domains are frequent motifs observed in proteins promoting multimerization towards homo-and also hetero-oligomers [START_REF] Crick | The packing of [alpha]-helices: simple coiled-coils[END_REF][START_REF] Kohn | Alpha-helical protein assembly motifs[END_REF][START_REF] Lupas | Coiled Coils -A Model System for the 21st Century[END_REF]. Proteins of the REMORIN family can oligomerize in vivo and in vitro into a filamentous state, possibly mediated by the coiled-coil domain [START_REF] Bariola | Remorins form a novel family of coiled coil-forming oligomeric and filamentous proteins associated with apical, vascular and embryonic tissues in plants[END_REF][START_REF] Perraki | Plasma membrane localization of Solanum tuberosum remorin from group 1, homolog 3 is mediated by conformational changes in a novel C-terminal anchor and required for the restriction of potato virus X movement[END_REF]. The in vivo functions of REMORIN oligomerization are not understood but they might include protein clustering to assure the nanodomain-mediated tasks. Nanoclustering of proteins is proposed to play an essential role in the segregation and functionalization of membrane rafts [START_REF] Simons | Membrane organization and lipid rafts[END_REF]. Subtype Group 1 REMORINs form trimeric coiled-coil superstructures in vitro [START_REF] Perraki | Plasma membrane localization of Solanum tuberosum remorin from group 1, homolog 3 is mediated by conformational changes in a novel C-terminal anchor and required for the restriction of potato virus X movement[END_REF] that lead to the formation of filamentous complexes [START_REF] Bariola | Remorins form a novel family of coiled coil-forming oligomeric and filamentous proteins associated with apical, vascular and embryonic tissues in plants[END_REF].

Our studies on the membrane anchoring mechanism and nanodomain targeting have recently revealed unconventional protein-membrane interactions mediated by the last 28 residues of the C-terminal region (REMORIN C-terminal anchor, REMCA) [START_REF] Gronnier | Structural basis for plant plasma membrane protein dynamics and organization into functional nanodomains[END_REF] that anchors to the surface of the plasma membrane without providing any transmembrane segment. The role of REMORIN oligomerization during this process remains obscure.

Magic-angle-spinning (MAS) solid-state nuclear magnetic resonance (SSNMR) and cryo-electron microscopy (cryo-EM) are both powerful emerging techniques useful to investigate assembled biomolecules in their native states. These techniques have the complementary characteristics of providing atomic data (reviewed for example in [START_REF] Habenstein | Solid-state NMR: An emerging technique in structural biology of self-assemblies[END_REF][START_REF] Loquet | Structural investigations of molecular machines by solid-state NMR[END_REF][START_REF] Meier | The structure of fibrils from 'misfolded' proteins[END_REF][START_REF] Tycko | Molecular structures of amyloid and prion fibrils: consensus versus controversy[END_REF][START_REF] Weingarth | Solid-state NMR-based approaches for supramolecular structure elucidation[END_REF]) and detailed shape / symmetry information (reviewed for example in [START_REF] Bai | How cryo-EM is revolutionizing structural biology[END_REF][START_REF] Earl | Cryo-EM: beyond the microscope[END_REF][START_REF] Jiang | Atomic cryo-EM structures of viruses[END_REF][START_REF] Vonck | Advances in high-resolution cryo-EM of oligomeric enzymes[END_REF])), respectively.

We have here taken advantage of several biochemical, biophysical and bioinformatic tools to decipher the role and the structure of REMORINs' assembly. We report that multimerization of REMORINs into trimers confers their membrane affinity and could participate in the nanoclustering into membrane domains. Establishing homogeneous filament formation of the protein core involved in oligomerization allowed us to combine SSNMR and cryo-EM to gain insights into the supramolecular architecture of oligomerized REMORIN filaments.

We can thus add another piece to the puzzle of the unconventional mechanism of membrane binding and nanodomain recruitment of the REMORIN family. The coiledcoil superstructures of the REMORINs hold functions resulting from the assembled complexes rather than from their monomeric subunits. E. coli BL21 (DE3) strains were transformed with a pET24-StREM1.3(86-198)-His 6 vector, containing the DNA encoding for residues 86-198 of REMORIN1.3 of Solanum tuberosum, and plated onto LB agar plates containing 30 mg/mL kanamycin. A flask containing 10 mL of LB medium was inoculated with a single colony and incubated overnight at 37°C under shaking at 200 rpm. The culture and expression of uniformly labelled 15 N, 13 C StREM1.3(86-198)-His 6 (hereafter called REM H6(86-198) ) was performed in M9 medium supplemented in 15 NH 4 Cl and 13 C 6 -Dglucose as nitrogen and carbon sources. The labelled M9 medium was inoculated with 2% (v/v) of the unlabelled LB preculture and incubated at 37°C under shaking until the OD 600 reaches 0.7-0.8. At this stage, 1 mM IPTG was added to the culture for 20h protein expression at 18°C. Cells are then harvested by centrifugation (6000 g, 10 min, 4°C) and resuspended in the lysis buffer (20 mM HEPES, 150 mM NaCl, 20 mM imidazole, 1 mM PMSF, 1 tablet of Complete (Roche), 0.02 % NaN 3 , pH 7.4).

Material and Methods

Expression, purification and filament formation of recombinant

Cells were lysed by sonication on ice for 3 minutes, 30% magnitude (30 sec pulses, recover the supernatant. The affinity purification was realised with the Äkta Pure 25 HPLC system (GE Healthcare Life Sciences) using a His-trap HP affinity column equilibrated with 5 column volumes of washing buffer (20 mM HEPES, 150 mM NaCl, 20 mM imidazole, 0.02 % NaN 3 , pH 7.4). The protein was eluted with a linear gradient from 0 to 100 % of elution buffer (20 mM HEPES, 150 mM NaCl, 500 mM imidazole, 0.02 % NaN 3 , pH 7.4) which led to a final yield of 10 mg of pure 15 N, 13 C labeled protein per liter of culture (Figure S1). Protein filaments were obtained by dialyzing overnight at room temperature the eluted sample against the assembly buffer (10 mM HEPES, 50 mM NaCl, 0.02 % NaN 3 , pH 7.4). Protein filaments of REM , i.e. REM H6(86-198) lacking the N-terminal His 6 -tag, were prepared as follows. Affinity purification was performed as described above using a different washing buffer (50 mM Tris, 100 mM NaCl, 0.5 mM EDTA, 1 mM DTT, 0.5 mM PMSF, 0.02% NaN 3 , pH=8.1) and elution buffer (50 mM Tris, 100 mM NaCl, 0.5 mM EDTA, 1 mM DTT, 0.5 mM PMSF, 500 mM imidazole, 0.02% NaN 3 , pH=8.1).

REM H6(86-198) contains a TEV cleavable His 6 -tag at its N-terminus. Eluted proteins and the TEV protease were mixed in a 100:1 (w/w) ratio, incubated at 4°C overnight under shaking and dialyzed against 1L of assembly buffer as described above to obtain REM filaments.

For ssNMR studies, filaments of REM lacking the N-terminal His 6 -tag were prepared with a different protocol to enhance purity / yield of the sample. Expression and affinity purification was performed as described for filaments of uniformly 15 N, 13 Clabelled REM H6(86-198) . Eluted protein fractions were pooled and adjusted to 0.5 mM EDTA and 1 mM DTT. Eluted proteins and TEV protease were mixed in a 100:1 (w/w) ratio, incubated at room temperature for 3h under shaking and dialyzed overnight at 4°C against the following buffer: 20 mM HEPES, 150 mM NaCl, 0.02% NaN3, pH=7.4. After dialysis, urea was added to the proteins up to 7 M urea and REM was retrieved on a His-trap HP column with the following washing buffer: 20 mM HEPES 150 mM NaCl 7 M urea pH=7.4; and elution buffer: 20 mM HEPES, 150 mM NaCl, 7 M urea, 500 mM imidazole, pH=7.4. REM was enriched in the flowthrough. and TEV protease, containing a His 6 -tag, bound to the column and were then eluted. Fractions containing REM were pooled and concentrated in an Amicon Ultra-15 centrifugal filter unit with MWCO = 3 kDa up to 0.8 mM, then dialyzed at room temperature against the assembly buffer (10 mM HEPES, 50 mM NaCl, 0.02% NaN3, pH=7.4). The sample was then incubated at 37°C for 3h under shaking then at room temperature for 7 days.

Coiled-coil prediction and sequence alignment coiled-coil domain was identified using the software COILS [START_REF] Lupas | Predicting coiled coils from protein sequences[END_REF] and helical wheel diagrams for coiled-coils were built with DrawCoil 1.0 (http://www.grigoryanlab.org/drawcoil/). Arabidopsis REMORINs were retrieved from [START_REF] Raffaele | Genome-wide annotation of remorins, a plant-specific protein family: evolutionary and functional perspectives[END_REF]. Protein alignment was computed using MUltiple Sequence Comparison by Log-Expectation (MUSCLE; [START_REF] Edgar | MUSCLE: multiple sequence alignment with high accuracy and high throughput[END_REF])) using BLOSUM62 matrix, an -sv profile scoring method with following parameters; Anchor spacing:32, diagonal break:1, diagonal length:24, diagonal margin:5, gap extension penalty:-1, gap open penalty:-12, hydro:5 and hydro factor1.2) through the JABAWS server [START_REF] Troshin | Java bioinformatics analysis web services for multiple sequence alignment--JABAWS:MSA[END_REF]. Sequence alignments were rendered via the Jalview plugin [START_REF] Waterhouse | Jalview Version 2--a multiple sequence alignment editor and analysis workbench[END_REF] (Figure S2).

Microsomal purification and Western blot analyses

Microsomal and soluble fractions from Nicotiana benthamiana leaves expressing GFP-StREM1.3 constructs were prepared as described in [START_REF] Perraki | Plasma membrane localization of Solanum tuberosum remorin from group 1, homolog 3 is mediated by conformational changes in a novel C-terminal anchor and required for the restriction of potato virus X movement[END_REF]. Each fraction was analyzed by western blot using antibodies against StREM1.3 (Raffaele et al., 2009).

In vivo confocal imaging

Live cell imaging was performed using a Leica SP5 confocal laser scanning microscopy system (Leica, Wetzlar, Germany) equipped with Argon, DPSS and He-Ne lasers and hybrid detectors. Two days after agro-infiltration N. benthamiana leaves samples were transferred between a glass slide and a cover slip in a drop of water. GFP fluorescence was observed with excitation wavelengths of 488 nm and emission wavelengths of 490 to 550 nm. Experiments were performed using strictly identical confocal acquisition parameters (e.g laser power, gain, zoom factor, resolution, and emission wavelengths reception), with detector settings optimized for low background and no pixel saturation. Pseudo-colored images were obtained using the "Red hot" look-up-table (LUT) of Fiji software (http://www.fiji.sc/).

TEM and cryo-EM

REM H6(86-198) filaments were applied to previously glow-discharged carbon-coated copper 300 mesh grids and stained with a 2% uranyl acetate (w/v) solution. REM H6(86- 198) filaments were also prepared for cryo-EM. For the latter, lacey carbon copper grid were submitted to a standard glow discharge procedure and flash-frozen into a liquid ethane bath using EM GP (Leica). Specimens were observed under low-dose conditions using a cryo holder (Gatan, USA). Observations were performed with a FEI Tecnai F20 electron microscope operating at 200 KV and images were acquired using a digital 2k x 2k USC1000 camera (GATAN). Measurements of the repetitive patterns were calculated on FFT images of 10 selected areas. REM filaments lacking the N-terminal His 6 -tag were stained and applied to carbon-coated copper grids as described above. TEM images were recorded under low-dose conditions on a FEI CM120 120 kV FEI electron microscope using a Gatan USC1000 2k X 2k camera.

3D modelling

The 3D model of was constructed by homology using the server I-TASSER (https://zhanglab.ccmb.med.umich.edu/I-TASSER/) [START_REF] Zhang | I-TASSER server for protein 3D structure prediction[END_REF]. Coiledcoil trimers were assembled using the SymmDock server [START_REF] Schneidman-Duhovny | PatchDock and SymmDock: servers for rigid and symmetric docking[END_REF] and a supplementary energy minimization step was performed with Yasara (http://www.yasara.org/minimizationserver.htm) [START_REF] Krieger | Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8[END_REF].

Solution NMR spectroscopy.

1 H, 15 N SOFAST-HMQC (band-Selective Optimized-Flip-Angle Short-Transient Heteronuclear Multiple-Quantum Correlation [START_REF] Schanda | SOFAST-HMQC experiments for recording twodimensional heteronuclear correlation spectra of proteins within a few seconds[END_REF]) experiment was carried out at 313 K on a protein sample containing 200 μM REM , 20 mM HEPES, 150 mM NaCl, 0.02% NaN3, pH 7.4. Spectra were recorded on a Bruker Avance Neo 700 MHz spectrometer equipped with a 5 mm TXI 1H/13C/15N/2H probe. The spectrum was acquired with 512 scans, 128 and 2048 complex points in F1 and F2 dimensions, respectively. Frequencies were calibrated according to DSS signal. The NMR data were processed using the TOPSPIN 4 software (Bruker Biospin).

Solid-state NMR spectroscopy

MAS solid-state NMR experiments were performed on a 600 MHz ) and 800 ) 1 H Larmor frequency spectrometer (Bruker Biospin) using 3.2 mm MAS probes. Solid-state NMR rotors were filled with 10 mg of REM H6(86-198) or ) filaments and span at 11 kHz for spectra acquisition. Sample temperature was set between 0-5°C according to DSS signal used as an internal reference [START_REF] Bockmann | Characterization of different water pools in solid-state NMR protein samples[END_REF]. A ramped CP with a 1 ms ) and 0.7 ms ) contact time was used for the 1 H-13 C cross-polarization (CP)

transfer. An acquisition time of 20 ms and 2k scans were used for 1D 1 H-13 C CP. For the 2D spectrum, the 13 C-13 C polarization transfer was performed with proton-driven spin diffusion (PDSD) applied for a mixing time of 50 ms to detect intra-residue correlations. Proton decoupling during the acquisition was achieved using a SPINAL-64 decoupling sequence [START_REF] Fung | An improved broadband decoupling sequence for liquid crystals and solids[END_REF]. Acquisition times of 20 ms and 6.5 ms ), 20 ms and 7.5 ms ) were chosen for the direct and indirect dimensions, respectively. The total number of scans was 960 and 640 for a total experiment time of 6 and 7 days approximately, for REM H6(86-198) and REM respectively. Secondary chemical shifts were calculated using random coil chemical shifts described by Wang and Jardetzky [START_REF] Wang | Probability-based protein secondary structure identification using combined NMR chemical-shift data[END_REF]. All data were processed using Topspin 3.2 (Bruker Biospin) and analyzed with CCPNMR Analysis software [START_REF] Skinner | CcpNmr AnalysisAssign: a flexible platform for integrated NMR analysis[END_REF]. Backbone and sidechains 13 C chemical shifts were predicted from the protein 3D coordinates of the coiled-coil model spanning residues 117-166 using the software ShiftX2 [START_REF] Han | SHIFTX2: significantly improved protein chemical shift prediction[END_REF].

Results

REMORIN coiled-coil trimerization promoted by the highly-conserved C-terminal is essential for membrane targeting

REMORINs share a highly variable N-terminal domain in terms of length and conservation of the primary sequence and a highly conserved C-terminal region [START_REF] Bariola | Remorins form a novel family of coiled coil-forming oligomeric and filamentous proteins associated with apical, vascular and embryonic tissues in plants[END_REF][START_REF] Raffaele | Genome-wide annotation of remorins, a plant-specific protein family: evolutionary and functional perspectives[END_REF][START_REF] Reymond | Cloning of a cDNA encoding a plasma membrane-associated, uronide binding phosphoprotein with physical properties similar to viral movement proteins[END_REF]. Previous work on a Solanum tuberosum REMORIN of group 1 isoform 3 (StREM1.3) showed that the Cterminal region encompasses two distinct domains (Figure 1A): the short C-terminal anchor [START_REF] Perraki | Plasma membrane localization of Solanum tuberosum remorin from group 1, homolog 3 is mediated by conformational changes in a novel C-terminal anchor and required for the restriction of potato virus X movement[END_REF] specifically binds plasma membrane phosphatidylinositol 4-phosphate (PI4P) and mediates StREM1.3 PM nanodomain organization [START_REF] Gronnier | Structural basis for plant plasma membrane protein dynamics and organization into functional nanodomains[END_REF], while the region [86-170] contains a segment predicted to be implicated in the oligomerization of the protein [START_REF] Bariola | Remorins form a novel family of coiled coil-forming oligomeric and filamentous proteins associated with apical, vascular and embryonic tissues in plants[END_REF][START_REF] Perraki | Plasma membrane localization of Solanum tuberosum remorin from group 1, homolog 3 is mediated by conformational changes in a novel C-terminal anchor and required for the restriction of potato virus X movement[END_REF]. The latter domain displays a strong propensity to form a coiledcoil super helix (Figure 1B). In such structures, alpha helical monomers wind around each other to optimize protein-protein contacts and stabilize the multimer assembly [START_REF] Crick | The packing of [alpha]-helices: simple coiled-coils[END_REF][START_REF] Kohn | Alpha-helical protein assembly motifs[END_REF]. All the members of the REMORIN family share these predicted structural properties and have strong sequence similarities between their C-terminal regions [START_REF] Raffaele | Genome-wide annotation of remorins, a plant-specific protein family: evolutionary and functional perspectives[END_REF]. We compared and analyzed the sequences of StREM1.3 with 5 Arabidopsis thaliana REMORINs from different groups (Groups 1, 3, 4, 5 and 6) to identify conserved elements that might be essential to the formation of coiled-coil super-helices (Figure 1B andS2). This comparison revealed a sequence similarity of 59 % with the most divergent, i.e.

AtREM3.1, and 94% sequence similarity with AtREM1.3. Based on the chemical properties of the amino acids, we identified three candidates that could potentially be involved in the helix-helix interfaces within the coiled-coil domain (L126, L137 and L155). The hydrophobic character of the residues at these three positions is highly conserved between the different members of the REMORIN family. Figure 1C shows the coiled-coil helical wheel at position L155 for the StREM1.3 trimer. L152 and L155 form a hydrophobic patch, which stabilizes the coiled-coil structure. Polar and charged residues (H156, K157 and E158) are exposed either to the solvent or to an opposite charge residue, again potentially stabilizing the structure. The protein loading control is presented in Figure S8.

We have recently reported structural insights into the role of the C-terminal peptide REM-CA during PM anchoring and nanodomain organization [START_REF] Gronnier | Structural basis for plant plasma membrane protein dynamics and organization into functional nanodomains[END_REF] and we here aimed at testing the impact of StREM1.3 trimerization on its membranebinding properties. Based on the sequence analyses, we introduced Prolines, in order to destabilize the coiled-coil superstructure [START_REF] Chang | Proline affects oligomerization of a coiled coil by inducing a kink in a long helix[END_REF] 1D). As expected, the wild-type protein localized exclusively at the plasma membrane. However, for all mutants, fluorescence was detected in the cytosol, which indicates that the protein is no longer strictly associated with the plasma membrane. To further confirm this observation we performed cell fractionation of tissues expressing either GFP-StREM1.3, GFP-StREM1.3 L126P , GFP-StREM1.3 L137P , or GFP-StREM1.3 L155P (Figure 1E). Western blotting with anti-REM antibody [START_REF] Raffaele | Remorin, a solanaceae protein resident in membrane rafts and plasmodesmata, impairs potato virus X movement[END_REF] clearly confirmed that StREM1.3 is only found in the microsomal fraction whereas the three mutants are visible in both microsomal and soluble fractions. Altogether, these observations suggest that oligomerization might be required for REMORINs' PM localization.

Building blocks of assembled REM H6(86-198) form organized fibers as revealed by TEM and cryo-EM

To investigate the assembly features and the structure of REMORIN oligomers, we focused on the conserved C-terminal region (residues 86 to 198), including the coiled-coil domain that most likely is responsible for the assembly [START_REF] Bariola | Remorins form a novel family of coiled coil-forming oligomeric and filamentous proteins associated with apical, vascular and embryonic tissues in plants[END_REF], and REMCA. We expressed and purified StREM1.3 C-terminal moiety ) in E. coli. After the purification, we recovered different fractions containing REM H6(86-198) protein and analyzed them with gel-electrophoresis under denaturing conditions (Figure S1). The fractions containing pure REM H6(86-198) were pooled and the self-assembly was triggered by dialysis. In order to provide an indepth analysis of the mesoscopic shape as well as structural features of the assembly, we chose several biophysical techniques including negative staining transmission EM (TEM), cryo-EM and SSNMR. TEM and cryo-EM gave us first insights into the morphology of REM H6(86-198) assemblies (Figure 2A and B, respectively), revealing ordered REMORIN fibers with a repetitive pattern. The repeating elements are propagated both horizontally and vertically. The periodicities correspond to 13 nm along and approximately 5 nm perpendicular to the fiber axis.

Interestingly, along the fibers, their width can vary (Figure S3) and their endings are irregular. The variable width engendering irregular endings of the fibers suggests an association and juxtaposition of thin filaments (arrow Figure 2C). This implies that the thin filaments self-assemble into large fibers in a constructive manner leading to the repetitive patterns.

Importantly, filaments of REM without the N-terminal His 6 -tag do not show specific lateral association (Figure 2D). However, the SSNMR data on REM lacking the His 6 -tag clearly confirm that the molecular structure of REM in laterally associated ) and single filaments ) does not vary essentially, because the spectral fingerprint is conserved between both filaments (Figure S4). The lateral association of REM H6(86-198) filaments allows for the analysis of the repetitive patterns promoted by the His 6 -tag, indicative of the assembly features. To decipher the molecular basis underlying the filament formation, we performed a 3D modelling of the StREM1.3 coiled-coil domain based on sequence homology using the I-TASSER server [START_REF] Zhang | I-TASSER server for protein 3D structure prediction[END_REF]. The modelling was restrained to the region with the higher coiled-coil propensity defined by the software COILS [START_REF] Lupas | Predicting coiled coils from protein sequences[END_REF]. The 3D model converged to a regular alpha helix composed of SSNMR data on REM H6(86-198) filaments support the 3D model of the coiled-coil domain and identify a short segment in β-strand conformation

To determine whether the 3D model obtained in silico reflects the structure adopted by REM H6(86-198) in the filaments, we analyzed 15 N, 13 C-labelled filaments using liquid and solid-state NMR. Solution NMR analysis revealed that REM H6(86-198) is unstructured in solution and adopts a 3D fold only upon assembly into multimeric complexes (Figure S5). SSNMR is an emerging technique providing atomic structural information on molecular assemblies in their native states. We conceived a protocol to successfully produce, purify and assemble suitable quantities (~10mg) of 15 N-and 13 C-labelled REM H6(86-198) in vitro to conduct multidimensional MAS SSNMR on the REMORIN in its filamentous state. The very intense signal observed in a onedimensional 1 H-13 C cross-polarization spectrum revealed the presence of a rigid core in the structure of the assembled proteins (Figure S6). A two-dimensional 13 C-13 C PDSD (proton-driven spin diffusion), Figure 3B, acquired with a short mixing time (50ms) set up to detect intra-residues 13 C-13 C correlations, reveals the SSNMR fingerprint of the REM H6(86-198) structure in filaments. The intense signals again indicate that the filaments contain a rigid protein core and the appearance of wellresolved individual peaks points to a well-defined atomic structure adopted by the monomers composing the assembly. Our in silico 3D model of REM suggests that the REMORIN monomers oligomerize into α-helical coiled-coil trimers promoted by the coiled-coil domain. Based on the 3D model, we used the ShiftX2 software [START_REF] Han | SHIFTX2: significantly improved protein chemical shift prediction[END_REF] to predict the expected protein 13 C chemical shifts for the coiledcoil domain REM and reported them on the 2D 13 C-13 C SSNMR spectrum recorded on REM H6(86-198) filaments (Figure 3B, red crosses, left spectrum). The majority of the predicted chemical shifts match well with the experimentally observed signals. This strongly supports the model of a α-helical tertiary fold in the filament core, reflecting the coiled-coil domain. However, a non-negligible part of the signals in the 2D spectrum remain unassigned and do not appear in spectral regions where signals from α-helical structural segments are located (Figure 3B, blue crosses, right spectrum). Among them, a detailed analysis was possible for Thr, Glu, Lys, Val, Ser, Leu, Ile, and Ala amino acids for which the resonances reflecting β-strand secondary structures are isolated in the spectrum. By comparing these residue-specifically assigned amino acids with the primary sequence of REM H6(86-198) we identified the unique motif in the REM H6(86-198) primary sequence (T 86 -EKRVSLIKA-A 95 ) adjacent to the coiled-coil region. The theoretical sequential peaks are all visible even if their assignments remain ambiguous. The local conformation of these residues was identified via their secondary chemical shifts that reflect a β-strand conformation where negative values were obtained. The unique amino acid composition of T 86 -EKRVSLIKA-A 95 and the clear β-strand conformation of the identified residues indicate the occurrence of a short β-segment at this location (Figure 3D). The assignment of the R89 spin system remained ambiguous because of the important peak overlap in this spectral region. Likewise, the region around T 86 -A 95 region might be extended but the chemical shifts of these residues could not be identified due to spectral overlap.

Cryo-EM data suggest that trimeric coiled-coil REM H6(86-198) arranges itself as aligned building blocks into filaments Cryo-EM micrographs revealed the repetitive motifs in the REM H6(86-198) filaments and the spectral resolution we obtained on the filaments allowed for the detailed measurement of their dimensions (Figure 4A). The longitudinal repetitions are characterized by 13 nm in length with a width of approximately 5 nm. The dimensions of the 3D model of the REM (117-166) coiled-coil trimer, with 8 nm in length and a width of ~1.5 nm without side-chains (Figure 4A), tie in the distances obtained on the electron micrograph of the REM H6(86-198) fibers. The 3D model of the coiled-coil domain illustrates the tertiary fold of the REM (117-166) with the region spanning 132-166 showing well-defined α-helices, slightly twisted to allow protein-protein contacts at positions 155 and 137 (Figure 4A,B). The segment 117-131 is less ordered than expected for the coiled-coil domain but displays lower structure accuracy for the generated model (Figure S7). Figure 4B shows an example of close hydrophobic contacts between Leucine residues in the trimer at the position 155, essential for trimer stability. Based on the ensemble of our results we propose a cartoon model of the REM H6 (86- 198) building block in filaments, encompassing a coiled-coil domain, in which the three membrane-affinity impairing mutations are localized, flanked by a short β-strand and supposedly two segments with less well-defined structures on both sides (Figure 4A).

The N-terminal ending of the coiled-coil domain contains a region of less defined structure and a stretch of 10 amino acids, mainly composed of hydrophobic residues constituting a regular β-strand, whereas the C-terminal REM-CA seems to adopt a condensed, unstructured form. The two endings account for the regions of dense protein concentration in the EM micrographs. The proximity of the β-strands could stabilize the trimer through an inter-strand hydrogen-bonding network.

Discussion and Conclusion

Our results have demonstrated an unexpected impact of REMORIN oligomerization via the coiled-coil domain on membrane affinity and we therefore decided to tackle the structure of this best-characterized nanodomain-associated plant protein supramolecular assembly.

By using a coiled-coil prediction server, we identified the region with the highest propensity of forming a coiled-coil and we singled out three hydrophobic residues that could stabilize the trimeric architecture, L126, L137 and L155. In vivo monitoring of the localization of REMORIN and of the three substitution mutants by confocal microscopy showed that all three mutations strongly impair membrane affinity suggesting that trimer formation is correlated with REMORIN-membrane interactions and that a cooperative effect between REMORINs plays an important role in membrane binding.

Full-length REMORINs form filaments, which were proposed to arise from interactions between coiled-coil trimers of the conserved C-terminal region [START_REF] Bariola | Remorins form a novel family of coiled coil-forming oligomeric and filamentous proteins associated with apical, vascular and embryonic tissues in plants[END_REF]. We show that REM , lacking the highly variable N-terminal region, also assembles into well-organized filaments. When the N-terminal His 6 -tag of REM is conserved, i.e. REM H6(86-198) , the filaments arrange into fibers that vary in width over the filament length and end in a thin architecture. This behaviour is consistent with the association or dissociation of trimeric building blocks that build a strong intermolecular network of weak interactions. Importantly, these fibers show very homogeneous repetitive patterns in electron micrographs that enable the analysis of the molecular building blocks in the filaments.

Creating an in silico 3D model of the coiled-coil domain allowed us to visualize the underlying 3D structures, to identify stabilizing hydrophobic and electrostatic contacts and to estimate the dimensions of such coiled-coil trimer. In this model, the three Leucines are located in essential positions, explaining the destabilizing effect and membrane-affinity loss of Proline mutations, L126P, L137P and L155P.

To corroborate our working model of trimeric coiled-coil building units, we performed SSNMR on REM H6(86-198) and REM filaments, obtaining atomic structural data on the intact filaments. Based on the 3D trimeric model, we compared predicted with observed SSNMR chemical shifts, validating the hypothesis of trimeric coiled-coil units that associate laterally and, furthermore, residue-type and sequential assignments revealed longitudinal interactions between the trimers involving a short β-strand and less well-ordered condensed segments. The dimensions of the building blocks obtained from cryo-EM micrographs are compatible with the trimeric REM H6(86- 198) coiled-coil units, derived from the 3D model. Combining the structural data from SSNMR, the in silico model, and cryo-EM, we propose a cartoon model that reflects the architecture in REM H6(86-198) and REM filaments (Figure 4A).

Conclusions

Protein organization into membrane domains relies on complex and scarcely in-detail explored structural mechanisms, involving lipid-protein and protein-protein interactions [START_REF] Simons | Membrane organization and lipid rafts[END_REF]. Considered as genuine PM nanodomainorganized proteins in plants, REMORINs attach to the PM and concentrate in nanodomains by an original mechanism via the C-terminal anchor, REM-CA [START_REF] Gronnier | Structural basis for plant plasma membrane protein dynamics and organization into functional nanodomains[END_REF]. We here report that REM-CA alone is not sufficient and responsible for membrane targeting. We show that the conserved trimerization region plays a central role and we investigated the structure of this region by conceiving a protocol to obtain filaments of mesoscopic scale that we analyzed by various techniques. The obtained data and proposed cartoon model might also reveal other modes of interaction of REMORINs during oligomerization such as β-strand hydrogen bonding. However, the interactions between trimer coiled-coil domains promoting the filament assembly might reflect interactions that favour the highly specific nanoclustering occurring between REMORINs of the same family [START_REF] Jarsch | Plasma Membranes Are Subcompartmentalized into a Plethora of Coexisting and Diverse Microdomains in Arabidopsis and Nicotiana benthamiana[END_REF]. Our findings open an avenue to understanding the complex nanodomaintargeting mechanisms of REMORIN proteins based on the combination of SSNMR and cryo-EM and aim at contributing to the comprehension of molecular strategies employed by membrane-domain proteins to perform their cellular functions.

Figure 1 .

 1 Figure 1. Mutations of conserved amino acid motifs in the coiled coil impairs PM targeting. (A) Domain organization in StRem1.3. (B) Coiled-coil propensity of StRem1.3 Cterminal region (109-183) realised with COILS. Sequence alignment of REMORIN homologs in S. tuberosum and A. thaliana. Protein sequences are ranked according to the conservation score. Hydrophobic residues potentially involved in helix contacts are highlighted in red. Conserved amino acids between the different REMORINs are shown in blue (dark blue for the most conserved to light blue for the less conserved). (C) Helical wheel corresponding to a coiled-coil trimer centred to the 152-158 region. Residues type are colored as follows: yellow, hydrophobic; blue, basic; red, acid; green, polar. Wheels were drawn using DrawCoil 1.0. (D) Confocal images presenting secant views of N. benthamiana epidermal cells expressing GFP-StREM1.3, GFP-StREM1.3 L126P , GFP-StREM1.3 L137P , GFP-StREM1.3 L155P , transiently expressed in tobacco epidermal cells. Scale bars: 20 µm. (E) Tobacco leaf cells expressing each construct were fractionated into soluble (sol.) and microsomal (m) compartments by centrifugation. Proteins were analyzed by Western blot with antibodies against StREM1.3.

Figure 2 .

 2 Figure 2. Electron micrographs of REM H6(86-198) and REM (86-198) filaments. (A) Negative staining TEM and (B) cryo-EM show filaments of REM H6(86-198) with two repetitive patterns perpendicular to each other. Width and thickness of fibers vary over the length of the filaments. (C) CryoEM of the sharp extremity of a REM H6(86-198) fiber shows similar repetitive motifs. Scale Bar of (A), (B) and (C) is the same: 100 nm (D) Negative-staining TEM micrograph of thin filaments of REM (86-198) , lacking the N-terminal His6-tag. Scale Bar: 100 nm

   monomers were assembled into a trimer by geometry based docking using the SymmDock server[START_REF] Schneidman-Duhovny | PatchDock and SymmDock: servers for rigid and symmetric docking[END_REF]. Figure3Ashows the trimeric coiled-coil structure of the REMORIN obtained in silico. A patch composed of hydrophobic residues, which point towards the trimer core, defines the interface between the three monomers and electrostatic interactions stabilize the architecture.

Figure 3 .

 3 Figure 3. 3D model and solid-state NMR of REM H6(86-198) filaments. (A) 3D model of StREM1.3 coiled coil domain predicted with I-TASSER server (Zhang, 2008). Two monomers in magenta are represented in surface mode and the third monomer with cartoon. All the hydrophobic side chains in the monomer are colored in yellow. Residue contacts identified between in StREM1.3 coiled-coil domain are highlighted on the primary sequence (green, hydrophobic contacts; red and blue, electrostatic contacts). (B) 2D PDSD 13 C-13 C spectrum of REM H6(86-198) filaments (50 ms mixing time) showing back-calculated 13 C chemical shifts with ShiftX2 from the 3D model (left spectrum, red crosses) and assigned chemical shift from β-strands (right spectrum, blue crosses). (C) Secondary chemical shift ΔδCα-ΔδCβ of the

Figure 4 .

 4 Figure 4. 3D architecture of REM (86-198) inside the filaments and associated to PM nanodomains. (A) Cryo-EM micrograph of REM H6(86-198) fibers depicts 13 nm horizontal repetitive patterns corresponding to a regular assembly of REM H6(86-198) . Scale bar: 20nm. Schematic representation of REM H6(86-198) structure inside the filament (β-strands are represented as blue arrows and α-helices as rectangles) and 3D structural model of REM (86- 198) coiled-coil domain. (B) Example of protein contacts inside the coiled coil domain. αhelices are represented in cartoon and Leucine sidechains in sticks. (C) Schematic representation of REM (86-198) structure when associated to the PM nanodomains. Red stars represent point mutations, which impair the membrane localization of StREM1.3. α-helices are represented as rectangles. The C-terminal membrane anchor REM-CA contains a short α-helice. Point mutations for in vivo confocal imaging experiments are highlighted in red.

  Furthermore, incorporating the recently proposed model of the REM-CA peptide structure during nanodomain formation and the data obtained on the role and structure of coiled-coil trimers, we propose a cartoon model for the nanodomain-associated REM (86-198) (Figure 4C). The coiled-coil trimers are stabilized by intermolecular protein-protein interactions including the three Leucine-Leucine contacts of residues 126, 137 and 155 (Figure 4C, red stars).

Acknowledgments

We acknowledge the financial support by the ANR (ANR-13-PDOC-0017-01 to B.H. and ANR-14-CE09-0020-01 to A.L.), the IdEx Bordeaux "Investments for the future" (ANR-10-IDEX-03-02 to B.H.) and PEPS to B.H. and S.M., the European Research Council (ERC-2015-StG GA no. 639020 to A.L.) and the CNRS (IR-RMN FR3050). A.L. is supported by the Ministère de l'Enseignement Supèrieur et de la Recherche, France (MERS, doctoral grants). TEV protease was a gift from Lionel Beaurepaire and the work has benefited from the facilities and expertise of the Biophysical and Structural Chemistry plateform (BPCS) at IECB, CNRS UMS3033, Inserm US001, Bordeaux University http://www.iecb.ubordeaux.fr/index.php/fr/plateformestechnologiques »


Nathalie Lassau
email: nathalie.lassau@gustaveroussy.fr

Theó Estienne

Philippe De Vomecourt

Mikael Azoulay

John Cagnol

Balleyguier

Gabriel Garcia

Michael Majer

Emmanuel Jehanno

Raphaëlle Renard Penna

Corinne Balleyguier

François Bidault

Caroline Caramella

Thibaut Jacques

Frédérique Dubrulle

Julien Behr

Nicolas Poussange

Jonathan Bocquet

Sarah Montagne

François Cornelis

Marie Faruch

Bertrand Bresson

Serge Brunelle

Aurélie Jalaguier- Coudray

Nicolasamoretti

Alain Blum

Anita Paisant

Victor Herreros

Olivier Rouviere

Salim Si-Mohamed

Lucy Di Marco

Marc Garetier

Frédéric Pigneur

Antonin Bergère

Catherine Cyteval Aa

Laure Fournier Bb

Caroline Malhaire

Jean-Luc Drape Dd

Edouard Poncelet Ee

Corinne Bordonne

Hugo Cauliez

Jean-François Budzik

Martine Boisserie

Thibault Willaume

Sébastien Molière

Noémie Peyron

Faure Ll

Septimu Caius

Giurca Mm

Valérie Juhan

Thomas Caramella

Antoine Perrey

Florain Desmots qq Matthieu Faivre- Pierre Rr

Martine Abitbol

Raïssa Lotte Tt

Diana Istrati

Guenoun Daphné

Alain Vv

Luciani

Marc Zins

Jean-François Meder

Anne Cotten

Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI

Keywords: Artificial intelligence (AI), Meniscal tears, Breast lesions, Thyroid cartilage, Liver lesions, Renal cortex

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

World interest in artificial intelligence (AI) is growing rapidly due to the availability of large and scalable datasets in many industries, advances in computing power particularly central processing unit (CPU) and graphics processing units (GPI), and the never-ending release of new algorithms. New standards of machine learning, such as deep learning, have tremendous impact on radiologic activities [START_REF] Syeda-Mahmood | Role of big data and machine learning in diagnostic decision support in radiology[END_REF]. This major change seems to enable radiologists to leverage their value, efficiency, accuracy, and personal satisfaction [START_REF] Recht | Artificial intelligence: threat or boon to radiologists?[END_REF]. Thrall et al. have analyzed key success factors of such changes in radiology based on value created, including increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists [START_REF] Thrall | Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success[END_REF]. The strategic positioning of all participants (patients, radiologists, AI expert, information technology department) is crucial for a successful transition [START_REF] Liew | The future of radiology augmented with artificial intelligence: a strategy for success[END_REF]. The Canadian Association of Radiologists (CAR) and the French Society of Radiology (SFR) published a white paper discussing the importance of AI and the probable impact on the community of radiologists in the near future [START_REF] Thrall | Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success[END_REF][START_REF]Artificial intelligence and medical imaging 2018: French Radiology Community white paper[END_REF][START_REF] Tang | Canadian Association of Radiologists white paper on artificial intelligence in radiology[END_REF]. Private and public radiologists have worked together to include all modalities such as ultrasound, computerized tomography (CT), and magnetic resonance imaging (MRI) [START_REF] Beregi | Radiology and artificial intelligence: an opportunity for our specialty[END_REF]. At the same time, the European Union is reforming data protection legislation via the General Data Protection Regulation (GDPR), with new legislation implemented in May 2018 [START_REF] Pesapane | Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States[END_REF].

The goal of this data challenge, organized by the SFR during the 2018 Journées Francophones de Radiologie (JFR), was to create a structured dynamic with the following objectives: 1) teach radiologists the new rules of GDPR while building a large multicentric prospective database of ultrasound, CT, and MRI patient images, 2) build a network from May to September 2018 including public and private radiologists, researchers, start-ups, large companies, and students from engineering schools, and 3) provide all French stakeholders working together during 5 data challenges with a secured framework, offering a realistic picture of the benefits and concerns in October 2018.

Material and methods

Clinical questions

The feasibility of the challenge was assessed by a team of radiologists and data scientists, based on a bibliography of existing challenges and the state of the French ecosystem of radiology and AI. The goal was set for a minimum database of 600 2D images. The organ societies of the SFR were asked to propose questions for the challenge, considering four criteria. First, the clinical relevance was judged by the radiologists. A literature search was performed using Pubmed, Kaggle website and grand-challenge website for this kind of competition in order to avoid reproducing an already existing challenge. Data-scientists from a top French engineering school studied the feasibility of the challenge with the data provided.

The number of exams available per year for each question was estimated to assure it was possible to gather the images in a very short period of time. Only the validated challenges on a shortlist that would reach the 600-image database would be launched and available for teams. Five clinical questions regarding selection criteria were chosen by the SFR. The inclusion criteria for each medical question were defined by the organ society responsible of the question.

Security and data protection

Regarding GDPR, a French regulation office, Commission Nationale Informatique et Libertés (CNIL), was consulted to assure the project design was compliant. A methodology reference was chosen in order to ease the data collection. Patients were provided a letter with all necessary information. The data collected could only be used for the aim of this challenge, and only one image per exam was selected to avoid patient identification. An ethical chart was written by the AI group of the SFR and required the signature of every radiologist uploading exams to the database. The data was stored within the Gustave Roussy Institute server to guarantee the same security as to clinical patient data. Two separate phases were designed for the project: a collection phase, where data could only be uploaded to the server by users identified, and a competition phase, where only the treated and checked data could be downloaded by identified team leaders.

Communication and team gathering

The teams were to have at least one radiologist, one engineering student, and a research lab and/or company. The networks of the JFR and SFR were used to gather radiologists. For students and research labs, the networks of French Graduate Schools and Universities and Life Imaging were used.

Companies that subscribed to JFR and data analytics startups were informed. Each could subscribe online by completing a form with the information required. The organization team published the contestant's contact information and the teams were allowed to gather at their convenience. After three months, the challenge staff proposed team compositions to contestants as an icebreaker. Only the teams with full composition were allowed to participate and access the datasets. The inclusion criteria and tutorials were published on the challenge website. Data were uploaded by each radiologist directly to the website. Contestants could access the event details, the team's contact information, and the link to download datasets online. WordPress was used to develop the website, with different plug-ins for uploading data and user management.

Inclusion and challenge phases

When the inclusion phase began on May 15 th 2018, radiologists used several networks to participate in the data collection: the SFR, JFR website and newsletters, the challenge website, and the French organ societies.

To motivate the radiology centers, inclusion status was sent twice a week to all the radiologists registered online and graphs showing the inclusion dynamic were included. The teams received access to the first part of the datasets on September 15 th , and the second part on October 12 th . Work stations with internet access were available for each team within the congress center of the JFR. The competition was launched on October 14 th 2018, along with the publication of the validation dataset. Each team had one hour to send the result of their work. Winning teams were announced on October 15 th .

Results

Renal cortex (CT)

Because glomerular filtration, an important clinical assessment of renal functions [START_REF] Stevens | Assessing kidney function--measured and estimated glomerular filtration rate[END_REF], is the main function of the renal cortex, there has been a considerable interest assessing renal cortex volume and thickness [START_REF] Stevens | Assessing kidney function--measured and estimated glomerular filtration rate[END_REF][START_REF] Beland | Renal cortical thickness measured at ultrasound: is it better than renal length as an indicator of renal function in chronic kidney disease?[END_REF]. Indeed, renal cortical volume and thickness have been proven to be effective biomarkers for renal function in many clinical situations. Applications include identifying patients with kidney that function well in the context of evaluating potential kidney donors, choosing the best resection plane in case of partial nephrectomy for a better preservation of renal function for urological treatment, and the assessment of clinical outcomes post-operatively. A non-invasive evaluation of cortex thickness is therefore critical and automatic segmentation of the renal cortex could play a key role for functional and morphological renal assessments [START_REF] Li | Automatic renal cortex segmentation using implicit shape registration and novel multiple surfaces graph search[END_REF][START_REF] Artunc | Simultaneous evaluation of renal morphology and function in live kidney donors using dynamic magnetic resonance imaging[END_REF]. However, renal cortex segmentation is complex because the anatomy of renal cortex varies in shape and size, and is not easily distinguished from neighboring tissues (i.e. vessels and renal columns) because of similar intensity.

For this question, participants only had one task of performing automatic segmentation of the clinical cortex. The images were given in Niifti files as the segmentation mask. For the test set, participants returned a binary image with the same size and format as the images in the trial set. One scanner image in the coronal plane, oblique along the major axis, of the kidney before and after injection of contrast material was requested for upload.

Breast lesions (MRI)

Breast MRI is a key imaging tool for breast lesion diagnosis and characterization [START_REF] Greenwood | Clinical breast magnetic resonance imaging: technique, indications, and future applications[END_REF]. Major indications of breast MRI are cancer staging, clarifying findings in equivocal lesions, treatment evaluation after neoadjuvant chemotherapy, and cancer screening in high-risk women [START_REF] Greenwood | Clinical breast magnetic resonance imaging: technique, indications, and future applications[END_REF]. Breast MRI is known to have high sensitivity but sometimes lacks specificity. A challenge specific to staging breast cancer is assessing which contrast uptake is malignant vs. benign, as many types of contrast-uptakes are visible, and when normal breast tissue may also show multiple contrast-uptakes. In addition, breast cancer increasingly appears as multifocal lesions, especially in high-grade breast tumors or those with positive Her2 receptors. Thus, there is a need to improve breast lesion characterization on MRI, despite the current use of multimodal sequences such as T2-weighted imaging and diffusion-weighted imaging to improve breast lesion characterization [START_REF] Kim | Diagnostic usefulness of combination of diffusion-and T2-weighted imaging, including apparent diffusion coefficient in breast resions: assessment of histologic grade[END_REF].

The goal was to evaluate whether techniques of AI would improve breast lesion characterization on MRI [START_REF] Codari | artificial intelligence for breast MRI in 2008-2018: a systematic mapping review[END_REF]. For this question, there were two different tasks: the first was to predict the benign or malignant nature of the lesion, and the second was to classify the lesion among 17 types of lesions. Due to a small number of images for some lesions, we decided to have only 4 groups: glandular tissue, infiltrating ductal carcinoma, other benign lesions, and other malignant lesions. One slice of 3D native dynamic sequence with injection with the two breasts visible was provided. The lesion, proven histologically or with an anterior MRI, was requested for upload.

Liver lesions (ultrasound)

Because liver ultrasound is operator-dependent, anything that increases the standardization and robustness is useful. Until now, few studies in ultrasound were published using AI, and have been focused on breast ultrasound, fatty detection on liver, and thyroid nodules [START_REF] Wang | Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network[END_REF][START_REF] Byra | Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images[END_REF][START_REF] Sadoughi | Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review[END_REF].

For this reason, the radiologists on SFR explored the detection and characterization of liver nodules. This is often the primary concern during an abdominal examination and has not been incorporated into a challenge to date.

For this question, the participants had three different tasks: detect the presence of a lesion in the image, characterize the lesion as benign or malignant, and classify among 5 types of lesions (cyst, angioma, focal nodular hyperplasia [FNH], hepatocellular carcinoma [HCC] and metastasis). One cross-sectional image of the liver with the edge apparent and without rib artifacts or calipers was requested, the gold standard being one injected imaging exam or one biopsy.

Meniscal tears (MRI)

Meniscal tears have an annual incidence estimated between 9 and 16 per 10,000 patients [START_REF] Kim | Increase in outpatient knee arthroscopy in the United States: a comparison of national surveys of ambulatory surgery, 1996 and 2006[END_REF].

Tears can be either traumatic or degenerative in origin and affect the lateral and/or medial meniscus, and the orientation can be classified as vertical (longitudinal, radial, or oblique) or horizontal. Precise detection and characterization is crucial for proper orthopedic management [START_REF] Mordecai | Treatment of meniscal tears: An evidence based approach[END_REF]. MRI is an important noninvasive diagnostic tool and has a reported accuracy above 85%, although direct examination in arthroscopy remains the gold standard [START_REF] Crawford | Magnetic resonance imaging versus arthroscopy in the diagnosis of knee pathology, concentrating on meniscal lesions and ACL tears: a systematic review[END_REF]. Proton density or intermediate-weighted sequences with fat suppression are the standard of care for meniscal tear detection [START_REF] Lecouvet | Magnetic resonance imaging (MRI) of the knee: Identification of difficult-to-diagnose meniscal lesions[END_REF]. Due to a complex orientation in space, three planes (sagittal, axial and coronal) or 3D sequences are required to examine every part of the menisci.

However, meniscal tears can be subtle and certain kind of lesions are frequently missed [START_REF] Lecouvet | Magnetic resonance imaging (MRI) of the knee: Identification of difficult-to-diagnose meniscal lesions[END_REF].

Few peer-reviewed articles have focused on meniscus analysis in MRI using machine learning to date, with no prospective cohort focusing on meniscal tears published at the time of the data challenge [START_REF] Bien | Deep-learning-assisted diagnosis for knee magnetic resonance imaging[END_REF][START_REF] Norman | Use of 2D U-Net Convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry[END_REF].

One MRI slice per meniscus was included for the data challenge (proton density or intermediate-weighted sequences with fat suppression). To enable analysis of both the anterior and posterior horn of each meniscus, the sagittal plane was chosen, with quality control instructions to avoid partial volume effects on meniscus edges. Patients who underwent previous knee surgery were not included. Both menisci were included for each patient. These technical choices enabled high reproducibility in the inclusion process. The goal of this task was to detect and characterize meniscus lesions, with three different tasks: detect the presence of a lesion in the meniscus, localize the fissure (anterior horn or posterior horn), and characterize the orientation of the fissure (horizontal or vertical). MRI examination has to be performed in the sagittal plane, obtained at 1.5-or 3T, passing through the anterior horn of the meniscus, and the anterior and posterior horn should be clear and well separated.

Thyroid cartilage (CT)

Tumor invasion of the thyroid cartilage by laryngeal carcinoma worsens the prognosis after radiotherapy treatment and may lead to a preference for primary surgery [START_REF] Wolf | Induction chemotherapy plus radiation compared with surgery plus radiation in patients with advanced laryngeal cancer[END_REF]. Conversely, the absence of tumor invasion of the cartilage leads to the choice of radiotherapy, chemoradiotherapy, or chemotherapy followed by radiotherapy [START_REF] Forastiere | Concurrent chemotherapy and radiotherapy for organ preservation in advanced laryngeal cancer[END_REF][START_REF] Forastiere | Long-term results of RTOG 91-11: a comparison of three nonsurgical treatment strategies to preserve the larynx in patients with locally advanced larynx cancer[END_REF][START_REF] Janoray | Long-term results of a multicenter =randomized phase III trial of induction chemotherapy with cisplatin, 5-fluorouracil, ± docetaxel for larynx preservation[END_REF]. On CT, the presence of lysis and/or condensation is indicative of tumor invasion of thyroid cartilage and tumor contact with cartilage. The performance of CT (i. e., sensitivity and specificity for the diagnosis of thyroid cartilage invasion) differs widely in clinical studies depending on the T stage of the tumor and the presence of imaging review [START_REF] Hartl | CT-scan prediction of thyroid cartilage invasion for early laryngeal squamous cell carcinoma[END_REF][START_REF] Li | Overstaging of cartilage invasion by multidetector CT scan for laryngeal cancer and its potential effect on the use of organ preservation with chemoradiation[END_REF]. As a result, it is interesting to ask AI to identify abnormalities of thyroid cartilage so as to advise the radiologist and the multidisciplinary team. The objective of the challenge was to evaluate the feasibility of an analysis of thyroid cartilage abnormalities by artificial intelligence.

For this question, there was only one task: characterize the aspect of the thyroid cartilage as normal or abnormal (lysis or condensation). One scanner image of a patient with an initial check-up that warranted suspicion for laryngeal or hypopharyngeal tumors, was requested for upload. The slice should be at cartilage level and with a thickness of at least 1 mm.

Communication and team gathering

A total of 323 persons registered online for the challenge, comprised of 45 researchers, 81 students, 78 engineers, and 119 radiologists. A total of 26 teams were complete and validated with 181 persons: 27 researchers, 52 students, 59 engineers, and 43 radiologists. The website generated 5,230 sessions from June to October 2018. Inclusions began on May 15 th , and the first images were uploaded at the beginning of June. A total of 5,130 images were uploaded (Fig. 2) from 46 radiology services (30 public, 13 private, 3 cancer centers). The meniscus and renal cortex challenges collected more images than the others. The five challenge groups submitted more than 600 images each before September 15 th , and hence were selected for the challenge.

Data processing

For each medical question, we defined a score function (Fig. 1). Except for the renal cortex which was evaluated with the Dice Score, these score functions were linear combinations of the binary AUC of each label. For instance, for the breast question, we calculated 5 AUC (one for the benign/malignant and 4 for each type of lesions). The linear combinations of each clinical question were defined in agreement with the referent radiologist. The formula of the score as well as the Python file used were provided to each team.

When possible, all images were resampled to the same pixel size and cropped to the same size. The pixel size and the image size are shown in Table 1. To resample the data, dicom field PixelSpacing (0028, 0030) and the python library SimpleITK were used. A linear interpolator was used to do the resampling. Some images were not resampled due to two reasons: 1) The dicom field PixelSpacing was empty due to anonymization of the image by the radiologist, and 2) for ultrasound images, the dicom field Pixel Spacing is not relevant.

The images include markers which are used to determine the size of the image, and this does not allow an automatic process to standardize a dataset.

Image size was an important question: the bigger the images, the more difficult the questions were. Figure 3 shows a comparison of the same image centered on the meniscus.

For each clinical question, we performed tests in order to find an optimal size of images. To crop the image, two different methods were used. The first was based on a manual Python application developed specifically for the project, which was used for breast, thyroid and meniscus. For each image, the user clicks on the centers of the lesion and a box was drawn centered on the click. The cropped image was saved as a Nifti file. The Python application accelerates the process: keyboard keys were associated with functions like cropping, saving, or loading the image. This allowed us to process hundreds of images per hour. For the renal cortex, the crop was made automatically: there is a binary mask of the renal cortex, so that the crop can be made around a centroid. The ultrasound images were not cropped; all the information from the scan written on the images had to be deleted (Fig. 4). For this, a Python tool was used to detect the contour of the ultrasound inside the images and put 0 value outside the ultrasound. This code was adapted to different ultrasound systems as they do not use the same encoding. As a result, the participants had only raw data. The final step of data preparation was the pre-processing of the label files (Fig. 5). When the data was uploaded to the web site, the labels were saved in a unique CSV file. To have a clean label file, the following steps were needed: one unique label file was created for each clinical question, then all non-relevant information (date of upload) was deleted. Categorical labels were converted to binary labels, and all false characters were cleaned. The file quality and inclusion criteria were checked by a data-scientist and the referent radiologist for each challenge. The senior radiologist also replaced the incorrect labels. A total of 4,170 images could be used for the dataset (Table 2).

For each medical question, data were split into three datasets: train set, validation set, and test set (Table 3). The labels (lesion or not, malignant/benign) were equally distributed between the datasets in order to have the same proportion. Of the 26 teams, 23 submitted results (Table 4). Six prizes were announced on October 15th: LyPhTe team for renal cortex, RadioAdvisor and LyPhTe for meniscus fissure, Owkinautes for liver lesions, SynovIA for thyroid, and Owkinautes for breast. For each challenge, the winning team and its members were invited on the stage to present their method and algorithm and to receive their price: 3,000 euros and a publication in a medical journal.

Discussion

The JFR data-challenge covered ultrasound, CT and MRI with 5 clinical questions in parallel.

The contestants had to solve different type of tasks, including segmentation, detection, and characterization. The rate of inclusion of 5,170 images within 4 months was efficient with large mobilization of 46 centers. The 26 multidisciplinary teams with 181 contestants demonstrated impressive gathering of the four communities: radiologists, researchers, students, and companies. It can be noted that three challenges had very good results, with an AUC over 0.90. Between 6-24% of the images received had to be excluded from the datasets.

Publications from other challenges usually do not describe the database cleaning process. The amount of images cleaned out of the database can be explained by the team of radiologists and data scientists who rigorously analyzed the data quality. The data quality may have been increased by regular checkup of the data during the inclusion phase and by giving regular feedback to each medical center regarding the data uploaded.

There have been numerous data challenges with various formats, datasets, and prices.

A number of these challenges can be found in the Grand Challenge website. Most have the same characteristics: one modality (MRI for BRATS, CT for LUNA, Histology for BACH), one organ (Liver for LITS Challenge, Breast for CAMELYON17, and one task (segmentation for BRATS, Detection for RNSA Challenge) [START_REF] Aresta | BACH: Grand challenge on breast cancer histology images, submitted to medical image analysis -Publication licensed under the Creative[END_REF][START_REF] Bandi | From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge[END_REF][START_REF] Menze | The multimodal brain tumor image segmentation benchmark (BRATS)[END_REF][START_REF] Setio | Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge[END_REF].

Automatic segmentation is currently a main topic in medical images and machine learning. In regards to kidney, there are not as many publications compared to other organs like lungs or brain. The main difference is that most of the articles are based on a machine learning algorithm such as a graph or random forest [START_REF] Jin | 3D Fast automaticsegmentation of kidney based on modified AAM and random forest[END_REF][START_REF] Chen | An automatic method for renal cortex segmentation on CT images: evaluation on kidney donors[END_REF][START_REF] Yoruk | Automatic renal segmentation for MR urography using 3D-GrabCut and random forests[END_REF][START_REF] Xiang | CorteXpert: a model-based method for automatic renal cortex segmentation[END_REF], and not a deep learning-based method, which were used by the participants of our challenge. Some articles also use 3D MRI [START_REF] Yoruk | Automatic renal segmentation for MR urography using 3D-GrabCut and random forests[END_REF], although they have few images (under 100 patients).

Concerning breast lesions, the main technique used for data challenges is the mammography, the gold standard for detecting early-stage breast cancer before lesions are clinically detected. Several challenges have focused on breast cancer (Digital Mammography DREAM Challenge [START_REF]The Digital Mammography DREAM Challenge[END_REF], CAMELYON [START_REF] Bandi | From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge[END_REF], or BACH [START_REF] Aresta | BACH: Grand challenge on breast cancer histology images, submitted to medical image analysis -Publication licensed under the Creative[END_REF]), but they all focus only on 3 modalities: mammography, histopathology and ultrasound. To our knowledge, this is the first challenge assessing breast lesions using MRI images [START_REF] Araujo | Classification of breast cancer histology images using convolutional neural networks[END_REF][START_REF] Arevalo | Representation learning for mammography mass lesion classification with convolutional neural networks[END_REF][START_REF] Shan | Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods[END_REF].

For liver lesions, no data challenge was organized using US. The current theme is focused on detecting nonalcoholic fatty liver, a factor for the development of hepatocellular carcinoma. The researchers used a hepatic renal index defined as a ratio of average brightness level of the liver and the kidney cortex with a good AUC of 0.97. The author used 540 images from 54 patients (10 per patient), but until now, no publication was available on detection and characterization of focal lesion [START_REF] Byra | Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images[END_REF] Contrary to lung lesions and breast lesions, recent algorithms for machine learning have not been frequently used for the knee. In Bien et al. study, deep learning was used to detect anterior cruciate ligament tears and meniscal tears on knee MRI [START_REF] Bien | Deep-learning-assisted diagnosis for knee magnetic resonance imaging[END_REF]. This approach is similar to that used by the participants in our challenge except they did not predict the position and the orientation of the tears. Other articles focus on cartilage lesion or predicting osteoarthritis [START_REF] Liu | Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection[END_REF].

These five challenges gathered a large community of radiologists, engineers, researchers, and companies in a short period of time. The results on the three modalities with an AUC > 0.90 show AI is a promising field on the three radiology modalities. In the future, it could be useful to increase the number of patients with an equal distribution between normal and abnormal images.

Figure 1 .

 1 Figure 1. For each of the 5 challenges, the score was calculated following different selection criteria.

Figure 2 .

 2 Figure 2. For the 5 challenges, the number of images sent was monitored between June and September 2018.

Figure 3 .

 3 Figure 3. For the meniscal tears challenge, the images were resized. Figures show example of change in image resolution (a) Image of 256 × 128 pixels. (b) Image of 256 × 256 pixels. (c) Image of 384 × 256 pixels.

Figure 4 .

 4 Figure 4. The images of the liver lesion challenge were processed to facilitate the analysis. Figures show image before and after processing. (a) Ultrasound image before pre-processing. (b) Ultrasound image after pre-processing.

Table 1 .

 1 Pixel size and image size after pre-processing.

Table 2 .

 2 Comparison between images received and images kept for each dataset.

Table 3 .

 3 Train, validation, and test split.

Table 4 .

 4 Scores of each teams for the challenges.

		Pixel Size	Image size
	Cortex Renal	1 mm	192 * 192
	Breast	0.7 mm	440 * 440
	Meniscus fissure	0.332 mm	256 * 256
	Thyroid cartilage	0.45 mm	192*192
	Liver	/	/

Conflict of interests

The authors declare that they have no conflicts of interest relating to this paper.


Vincent Couteaux
email: vincent.couteaux@telecom-paristech.fr

Salim Si-Mohamed

Olivier Nempont

Thierry Lefevre

Alexandre Popoff

Guillaume Pizaine

Nicolas Villain

Isabelle Bloch

Anne Cotten

Loïc Boussel

Automatic knee meniscus tear detection and orientation classification with Mask-RCNN

Keywords: Knee meniscus, Artificial intelligence, Mask region-based convolutional neural network (R-CNN), Meniscal tear detection, Orientation classification

This work presents our contribution to a data challenge organized by the French Radiology Society during the Journées Francophones de Radiologie in October 2018. This challenge consisted in classifying MR images of the knee with respect to the presence of tears in the knee menisci, on meniscal tear location, and meniscal tear orientation.

Materials and methods:

We trained a mask region-based convolutional neural network (R-CNN) to explicitly localize normal and torn menisci, made it more robust with ensemble aggregation, and cascaded it into a shallow ConvNet to classify the orientation of the tear.

Results: Our approach predicted accurately tears in the database provided for the challenge. This strategy yielded a weighted AUC score of 0.906 for all three tasks, ranking first in this challenge.

Conclusion:

The extension of the database or the use of 3D data could contribute to further improve the performances especially for non-typical cases of extensively damaged menisci or multiple tears.

Introduction

Meniscal lesions are a frequent and common cause of knee pain, responsible for approximately 700,000 arthroscopic partial meniscectomies per year in the United States [START_REF] Cullen | Ambulatory surgery in the United States, 2006[END_REF].

They are defined as a tear within the meniscus, and can lead to articular cartilage degeneration over time, further necessitating surgical treatment. Magnetic resonance imaging (MRI) plays a central role in the diagnosis of meniscus lesions, the preoperative planning and the postoperative rehabilitation of the patient [START_REF] Pache | Meniscal root tears: current concepts review[END_REF][START_REF] Lecouvet | Magnetic resonance imaging (MRI) of the knee: Identification of difficult-to-diagnose meniscal lesions[END_REF]. As meniscal lesions are very frequent, their diagnosis could certainly benefit from a quantitative and automated solution giving more accurate results in a faster way. Computer-aided detection systems for meniscal tears were thus proposed whereby regions of interest in the image are extracted and classified based on handcrafted image features [START_REF] Boniatis | A computer-based system for the discrimination between normal and degenerated menisci from magnetic resonance images[END_REF][START_REF] Köse | An automatic diagnosis method for the knee meniscus tears in MR images[END_REF][START_REF] Ramakrishna | An automatic computer-aided detection system for meniscal tears on magnetic resonance images[END_REF][START_REF] Saygili | Meniscus segmentation and tear detection in the knee MR images by fuzzy c-means method[END_REF][START_REF] Saygili | Meniscus tear classification using histogram of oriented gradients in knee MR images[END_REF].

The Journées Francophones de Radiologie was held in Paris in October 2018. For the first time, the French Society of Radiology organized an artificial intelligence (AI) competition involving teams of industrial researchers, students and radiologists. This paper presents our contribution to the knee meniscus tear challenge, where participants had to classify sagittal MRI slices cropped around the knee depending on the presence of tears in anterior and posterior menisci and on their orientation (horizontal or vertical). We proposed a method that takes advantage of recent advances in deep learning [START_REF] Garcia-Garcia | A review on deep learning techniques applied to semantic segmentation[END_REF][START_REF] Lecun | Deep learning[END_REF]. More precisely, we propose to localize, segment, and classify healthy and torn menisci using a mask region-based convolutional neural network (R-CNN) approach that is cascaded into a shallow ConvNet to classify tear orientation.

Method

Knee meniscus tear challenge

Sagittal MR images centered around the knee were provided with the following annotations: Images of size 256 × 256, either of the medial or of the lateral plane of the knee, were provided, as illustrated in Figure 1a,b. The femur was always on the left and the tibia on the right, with the anterior meniscus at the top and the posterior meniscus at the bottom of the image. Horizontal tears appeared vertical and vice versa. The grey level scale was in an arbitrary unit scaled between 0 and 1, and the images did not have consistent brightness and contrast.

On MRI, meniscal tears appear as thin, hyperintense lines that cut across the menisci, but we can observe various hyper-intense signals in the menisci (Figure 1). For instance, hyper-intense lines that only partially cut across the menisci should not be classified as tears (Figure 2a). Moreover, the menisci may be barely visible (Figure 2b) or have multiple tears (Figure 2c). In the latter case, the provided orientation may be ill-defined.

Menisci are small structures and tears present as thin abnormalities within menisci on MRI. To facilitate tear detection, we first localized the menisci. However, the localization of the menisci was not part of the provided annotations. To efficiently perform this annotation, we chose to approximate menisci by triangles resulting in a coarse segmentation of menisci (Figure 3).

To detect tears in both menisci and identify their orientation, we opted for a cascaded approach. First, menisci were localized and tears were identified. Then the orientation of torn menisci was classified. For both tasks, we applied a morphological pre-processing, as described below, to enhance the relevant structures in the image.

Morphological pre-processing of images

In a first attempt to better understand the dataset and the classification task at hand, we trained simple neural network classifiers on the training dataset in order to classify the posterior (resp.

anterior) meniscus into healthy and torn cases. Both networks were based on a simplified VGG-like architecture: the whole image is taken as the input, on which four 2D-convolution/ ReLu/Maxpool layers are applied followed by two dense layers and a final softmax-activated output layer [START_REF] Simonyan | Very deep convolutional networks for largescale image recognition[END_REF]. Note that these neural networks were only meant to explore the given dataset.

An accuracy of 83% could readily be obtained (precision, 0.76; recall, 0.8). However, when analyzing the interpretation of the ConvNet classification, it appeared that the network used non-relevant features in the image to provide the result (Figure 4). This phenomenon was consistently observed on other images in the dataset and is probably attributed to the variability of the images. Given that the dataset is small, the network is not able to properly generalize on so few samples. This prompted us to consider pre-processing the images in order to bring robustness to the classification. The approach we propose is close to that for detecting white matter hyper-intensities in brain MRI [START_REF] Xu | White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning, Brainlesion: glioma, multiple sclerosis, stroke and Traumatic Brain Injuries[END_REF].

Since meniscus tears are primarily characterized by distinct morphological features, it seems reasonable to assume that any image pre-processing step that would retain these characteristics while limiting the influence of other structures may be beneficial. Figure 4c shows the result of applying a black top-hat morphological filter on the image of Figure 4a. A black top-hat filter outputs an image wherein the bright regions correspond to regions in the original image which are smaller than the structuring element and darker than their surroundings. As can be clearly observed, the torn posterior meniscus can still be identified. A ConvNet classifier trained on black top-hat filtered images shows performance similar to the initial one (accuracy, 83%; precision, 86%; recall, 67%) but is able to focus precisely on the meniscus region (Figure 4d). In this case, the saliency map clearly indicates that only the meniscus region is relevant for the classification.

Meniscus localization and tear detection

To localize both menisci and identify tears in each meniscus, we used the Mask R-CNN framework, a state-of-the-art approach for Instance Segmentation. It performs object detection, segmentation and classification in a single forward pass [START_REF] He | Mask R-CNN[END_REF]. We trained the model to detect and segment four objects: (i) healthy anterior meniscus; (ii), torn anterior meniscus;

(iii) healthy posterior meniscus, and (iiii), torn posterior meniscus. In this way, we obtained the localization of each meniscus, the classification of healthy vs. torn, and a classification score. We chose to perform the classification of tear orientation independently on the segmented meniscus region only, as explained below because the classes would have been too imbalanced otherwise (only 11 vertical tears in the anterior meniscus for instance).

We used a Mask R-CNN model pre-trained on the common object in context (COCO) dataset [START_REF] Lin | Microsoft COCO: Common objects in context[END_REF] whose input is a three channel image. We applied three white top-hat filters (the dual of the black top-hat filters described above) on original MRI slices with square structuring elements of size 5×5, 11×11 and 21×21 (Figure 5) to generate network inputs.

Note that we did not constrain the model to return exactly one result for each meniscus because the two menisci were correctly detected in almost all cases. We illustrate in Figure 6 the output of the Mask R-CNN. In Figure 6a, the two healthy menisci are properly detected.

In Figure 6b, the posterior meniscus is appropriately identified as torn. However, the posterior meniscus is too widely segmented and incorrectly labeled as torn (Figure 6).

Training

We fine-tuned a Mask R-CNN with a ResNet-101 backbone, pretrained on COCO dataset) [START_REF] He | Mask R-CNN[END_REF][START_REF] Lin | Microsoft COCO: Common objects in context[END_REF][START_REF] He | Deep residual learning for image recognition[END_REF]. The training was done using an Adam optimizer, 1.10 -3 learning rate and batches of 8 images, during 1000 epochs of 100 batches.

Ensemble aggregation

To improve the robustness of our model, we applied ensemble aggregation. We trained five models on random folds of the full training data set (1128 images) and retained five additional models trained on random folds of the first training batch only (the first 257 images). We aggregated the results differently for anterior and posterior menisci. We classified the anterior meniscus as torn when at least one network had detected a torn anterior meniscus, with a probability P ant (F) equal to the mean classification score of all detected torn anterior menisci by the ensemble. We classified the posterior meniscus as torn when the strict majority of the networks had detected a torn posterior meniscus. The probability P post (F) is equal to the mean classification score of all detected torn posterior menisci by the ensemble. We used different aggregation methods as a large majority of anterior menisci are healthy. Some networks may not have seen enough torn anterior menisci in order to recognize them.

Tear orientation classification

To classify the orientation of torn menisci as horizontal or vertical, we trained a neural network on images cropped to the bounding boxes of detected torn menisci, resized to 47 × 47 pixels. This network was fed with pre-processed patches, each input having five channels illustrated in Figure 7 • Dense Layer with 1024 units, ReLU activation, P = 0.5 dropout,

• Dense Layer with 1024 units, ReLU activation, P = 0.5 dropout,

• Dense Layer with 2 units and a softmax activation.

We trained this network on 246 torn menisci of the training database with a Stochastic Gradient Descent, 1.10 -3 learning rate and batches of 32 images, during 800 epochs (approximately 5 min). We validated the method on the remaining 54 cases and selected the model with the highest validation accuracy. The organizers therefore removed from the database cases where both menisci had tears and the following values were submitted for each image:

Results

Score and ranking

• Probability of a tear in any meniscus P(F),

• Probability that the tear (if any) is in the anterior meniscus P(Ant),

• Probability that the tear (if any) is horizontal P(H).

The Mask R-CNN ensemble outputs a probability P ant (F) that the anterior meniscus is torn, and a probability P post (F) that the posterior meniscus is torn, both being independent a priori.

This results in Equation 2 (E2)

E2 P(F) = P post (F) + P ant (F) -P post (F)P ant (F)

where P(Ant) is defined by Equation 3 (E3)

E3 P(Ant) = P ant (F)/(P ant (F )+P post (F)).

To obtain P(H), we applied the orientation classifier on the anterior meniscus when P(Ant) > 0.5 and on the posterior meniscus otherwise.

A test set of 700 images was used for ranking. We obtained a score of 0.906 and shared the first place with another team (score 0.903).

Visual inspection

In most cases, the prediction was in line with our interpretation as illustrated in Figure 8, but a few cases seemed suspicious. The resulting classification scores were almost binary, either very close to 1 or very close to 0, especially P(F). However, for some images, the predictor returned classification scores close to 0.5 (Figure 9).

Discussion

The knee meniscus tear challenge posed an image classification problem. Image classification tasks in computer vision aim to discriminate many classes from the prominent object in the image [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF]. However, in this problem, the classification result should be based on thin details at a specific location in the image. Moreover, only a small database was available. Training a standard classifier from the image may therefore result in sub-optimal performances as observed in our initial experiments.

We chose to localize and segment the menisci and perform the classification within the anterior and posterior menisci. We opted for a Mask R-CNN approach as it can perform both tasks jointly [START_REF] He | Mask R-CNN[END_REF]. Due to the class imbalance, we did not classify the tear orientation using this approach, but cascaded the Mask R-CNN into a shallow ConvNet to classify tear orientation [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF]. Moreover, we provided pre-processed images to the networks to focus on relevant parts of the images by enhancing the tears.

In conclusion, our approach ranked first in the challenge by predicting accurately tears in the database provided for the challenge. The extension of the database or the use of 3D data could contribute to further improve the performances especially on untypical cases such as very damaged menisci or multiple tears.

  (i), position of the image (medial or lateral); (ii), presence of a tear in the posterior meniscus; (iii), presence of a tear in the anterior meniscus; (iiii), orientation of the tear in the posterior meniscus (if any); (v), and orientation of the tear in the anterior meniscus (if any). Two training batches were provided; the first made of 257 images was shared one month before the conference and the other, made of 871 images, 2 days before the end of the challenge. The first batch contained 55/257 (21.4 %) images with horizontal posterior tears, 46/257 (17.9 %) with vertical posterior tears, 13/257 (5.1 %) with horizontal anterior tears and 8/257 (3.1 %) with vertical anterior tears. The second batch contained 107/871 (12.3 %) images with horizontal posterior tears, 60/871 (6.9 %) with vertical posterior tears, 8/871 (0.9 %) with horizontal anterior tears and 3/871 (0.3 %) with vertical anterior tears. The classes were imbalanced, with horizontal tears and posterior meniscus tears being more frequent, and a low number of anterior tears were available for training. We reviewed the database and removed any ambiguous annotations from the training set.

  : 1) unprocessed patch, 2) local orientation map, computed with σ = 3 (see below), 3) local orientation map, computed with σ = 1, 4) black top-hat transform, with a disk structuring element of radius 4 pixels, and 5) black top-hat transform, with a disk structuring element of radius 8 pixels. The local orientation map represents the angle of the smallest eigenvector of the Hessian matrix at each pixel. The Hessian matrix was computed with the second derivative of a Gaussian kernel, whose standard deviation σ is a parameter. Only 300 torn menisci were provided for training. Therefore, we trained a very shallow CNN based on a VGG-like architecture: • Convolution, 3 × 3 kernel, 8 filters, ReLU activation, • Max-pooling, 2 × 2, • Convolution, 3 × 3 kernel, 16 filters, ReLU activation, • Max-pooling, 2 × 2, • Convolution, 3 × 3 kernel, 32 filters, ReLU activation, • Max-pooling, 2 × 2,

  Teams were ranked according to a weighted average of the area under the ROC curves (AUC) of the tear detection task Det (tear in any meniscus), the tear localization task Loc (anterior or posterior) and the orientation classification task Or (horizontal or vertical), according to Equation 1 (E1): E1 Score = 0.4 × AUC(Det) + 0.3 × AUC(Loc) + 0.3 × AUC(Or)

Figure 1 .

 1 Figure 1. Database contains either medial e.g. (a) or lateral e.g. (b) MR images of the knee. (a-b) MR images shows healthy menisci. (c-f) MR images shows examples of tears as present in the database. (c) Horizontal tear in posterior meniscus, (d) Horizontal tear in anterior meniscus, (e) Vertical tear in posterior meniscus and (f), Vertical tear in anterior meniscus. Arrows point out tears.

Figure 2 .

 2 Figure 2. MR images illustrate challenging cases. (a) Potentially misleading lesion. (b) Barely visible meniscus. (c) Multiple tears in the same meniscus.

Figure 3 .

 3 Figure 3. Figure shows two MR images from the training database illustrating the menisci annotation process. Each colored dot is a vertex of a triangle that approximates the segmentation of a meniscus.

Figure 4 .

 4 Figure 4. (a) Image from the training database with a clearly visible torn posterior meniscus that was correctly classified by the ConvNet. (b) Same image with a superimposed saliency mask indicating that the network focuses on non-relevant regions and barely considers the posterior meniscus itself. (c) Image (a) after applying a black top-hat filter with a disk structuring element of radius 5 pixels. (d) Saliency map for the processed image.

Figure 5 .

 5 Figure 5. Pre-processing of the data used as input of the Mask R-CNN. (a) Original image. (b) 5 × 5 white top-hat. (c) 11 × 11 white top-hat. (d) 21 × 21 white top-hat.

Figure 6 .

 6 Figure 6. Output of Mask R-CNN. (a-b) Correct results. (c) Posterior meniscus incorrectly segmented and labeled as torn.

Figure 7 .

 7 Figure 7. Patch extraction for orientation classification. (a) Extracted patch, resized to 47 × 47. (b) Local orientation map, σ = 3. (c) Local orientation map, σ = 1. (d) Black top-hat, r = 4. (e) Black top-hat, r = 8.

Figure 8 .

 8 Figure 8. Prediction results on the testing batch. Most results seem correct, e.g. (a-b). However, some predictions are suspicious, e.g. (d-e). (a) No tear. (b) Horizontal tear on the posterior meniscus. (d) P(Ant) ~ 0.45 but the anterior meniscus looks torn. (e) P(F) ~ 0 but a tear is visible in the anterior meniscus. (c) Distribution of P(F). (f) Distribution of P (H) for cases satisfying P(F) > 0.5.

Figure 9 .

 9 Figure 9. Cases for which P (F) (a-c) or P (H) (d-f) were close to 0.5. (a) Tear on the anterior meniscus but a slice where the menisci are connected was selected which does not meet the inclusion criteria. (b) Damaged anterior meniscus, but the presence of a tear is unclear. Yet the algorithm focused on the anterior meniscus: P (Ant) > 0.99. (c) Untypical lesion on the anterior meniscus. (d-e) Extensively damaged meniscus. (f) Several tears in one meniscus.

Conflict of interests

The authors declare that they have no conflicts of interest concerning this article.