Clément Beitone
email: clement.beitone@univ-grenoble-alpes.fr

Jocelyne Troccaz
email: jocelyne.troccaz@univ-grenoble-alpes.fr

Multi-eXpert Fusion: An ensemble learning framework to segment 3d trus prostate images

Keywords: Prostate segmentation, 3D TRUS, Deep Learning, Ensemble learning

Purpose: Prostate segmentation of 3D TRUS images is a prerequisite for several diagnostic and therapeutic applications. Unfortunately, this difficult task suffers from high intra-and inter-observer variability, even for experienced urologists/radiologists. This is why automatic segmentation algorithms could have a significant clinical added-value. Methods: This paper introduces a new deep segmentation architecture consisting of two main phases: view-specific segmentations of 2D slices and their fusion. The segmentation phase is based on three segmentation networks trained in parallel on specific slice viewing directions: axial, coronal, sagittal. The proposed fusion network is then fed with the output of the segmentation networks and trained to produce three confidence maps. These maps correspond to the local trust granted by the fusion network to each view-specific segmentation network. Finally, for a given slice, the segmentation is computed by combining these confidence maps with their corresponding segmentations. The 3D segmentation of the prostate is obtained by re-stacking all the segmented slices to form a volume.

Results: This approach was evaluated on a database of 100 patients with several combinations of network architectures (for both the segmentation phase and the fusion phase) to show the flexibility and reliability of the framework. The proposed approach was also compared to STAPLE, to the majority voting strategy and to a direct 3D approach tested on the same database. The new method outperforms these three approaches on all evaluation criteria. Finally, the results of the Multi-eXpert Fusion (MXF) framework compare favorably with other state-of-the-art methods while these methods typically work on smaller databases. Conclusions: We proposed a novel MXF framework to segment 3D TRUS images of the prostate. The main feature of this approach is the fusion of expert networks results at the pixel level using computed confidence maps. Experiments conducted on a clinical database have shown the robustness and flexibility of this approach and its superiority over state-of-the-art approaches. Finally, the MXF framework demonstrated its ability to capture and preserve the underlying gland structures, particularly in the base and apex regions.

I. INTRODUCTION

Prostate cancer is the second most common cancer in men and the fourth most common cancer overall according to the data from GLOBOCAN 2020 [START_REF] Sung | GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[END_REF]. Prostate screening tests may include a Prostate Specific Antigen test (PSA) and a Digital Rectal Examination test (DRE), but only a biopsy examination, most often performed under ultrasound control (Figure I), can certify the presence or absence of cancer cells and allows determining the grade of the cancer.

For this reason, trans-rectal ultrasound (TRUS) examination of the prostate is an essential skill for urologists/radiologists. Accurate segmentation of the prostate from 3D TRUS images serves as the cornerstone of many approaches and therefore has an impact on treatment planning, diagnostic and therapeutic procedures for prostate cancer as well as other prostate diseases. The improvement of this step would, therefore, both benefit the entire treatment chain and contribute to the reliability of the examination as well as to the patient's comfort during it. However, manual segmentation of 3D TRUS images is time-consuming, subjective and with limited reproducibility.

It heavily depends on experience and has a large inter-and intra-observer variability [START_REF] Tong | Intra-and inter-observer variability and reliability of prostate volume measurement via two-dimensional and three-dimensional ultrasound imaging[END_REF]. To cope with these issues semi-automatic and automatic algorithms are highly expected by clinical teams. Several prostate segmentation approaches have been proposed over the past decades [START_REF] Yang | 3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning[END_REF][START_REF] Yang | 3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework[END_REF][START_REF] Karimi | Accurate and Robust Segmentation of the Clinical Target Volume for Prostate Brachytherapy[END_REF]. Nevertheless most of them deal with the segmentation of 3D MR images where the problem is considered a bit easier. Indeed, on TRUS images, prostate boundaries are often weakly present or absent, especially at the base and apex, and various types of artifacts may be present.

Methods based on deep learning techniques, such as 2D and 3D Convolutional Neural Networks (CNN), have received considerable attention in recent years [START_REF] Haskins | Deep learning in medical image registration: a survey[END_REF]. As an instance for the 3D approaches Wang et al. [START_REF] Wang | Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound[END_REF] proposed a 3D CNN based on deep attentive features to segment prostate in 3D TRUS images. Once trained, these approaches are very efficient and provide results almost instantaneously, which again, in the clinical context, translates into shorter time for the examination and therefore less discomfort for the patient. Nevertheless, to be effective, these methods must be trained and evaluated on very large datasets. Moreover, their reliability and potential clinical use is only made possible if these datasets represent the variability of routine clinical cases.

Several 3D US image segmentation approaches use one or more CNN models working on 2D slices of a 3D image and combine the results into a 3D object. One advantage of these approaches is that training CNNs on 2D slices extracted from 3D volumes increases the size of the training database, with each volume generating several dozen of images. Among these 2D approaches, some also combine the result from several views. The rationale is that the visibility of an organ can vary a lot locally: for example, the apex of the prostate is not very visible in axial view but more clearly appears in a sagittal one. These combination techniques can be classified in a framework called ensemble

Superior Midgland

Inferior Apex Base

Prostate Sectors

Figure 1. TRUS-guided biopsy procedure -general set-up and typical axial TRUS image learning [START_REF] Svensén | Pattern recognition and machine learning[END_REF]. Indeed, it is often found that better performance can be achieved by combining several models in some way, instead of using a single one.

In an ensemble learning strategy, several models, also called experts, are trained in order to partially solve the global problem considered too complex to be solved at once. Then a supervision algorithm is used to combine the votes of these models. Such a strategy proves to be reliable and several approaches of this type can be found in the literature; each of them having shown their superiority over direct 3D approaches. For example, Man et al. [START_REF] Man | Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net[END_REF] segmented the pancreas with a set of three deformable U-Nets that handles the three main views (axial / coronal / sagittal) of a 3D image and fuses their output with the majority voting strategy. In a majority vote, a voxel is considered to belong to a class if more than fifty percent of the experts agree on this label. With a broader spectrum of application, Perslev et al. [START_REF] Perslev | One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation[END_REF] proposed a generic approach evaluated on ten different segmentation tasks of the Medical Segmentation Decathlon. Their strategy is based on training a U-Net on six different randomly selected views and combining the 6 output volumes using linear fusion. Finally, Orlando et al. [START_REF] Orlando | Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images[END_REF] have developed a deep learning version of a previously proposed work [START_REF] Qiu | Rotationally resliced 3D prostate TRUS segmentation using convex optimization with shape priors[END_REF] where they merge the segmentation results of a modified U-Net from twelve 2D views from 3D TRUS images and combine them through interpolation to segment the prostate in 3D. Nevertheless, the main drawback of this latter approach lies in the effect of the sampling and smoothing which may have a negative impact on very irregular prostates. More recently, Jiang et al. [START_REF] Jiang | Segmentation of 3D ultrasound carotid vessel wall using U-Net and segmentation average network[END_REF] proposed a segmentation average network for the fusion of three 2D segmentation maps in order to segment carotid vessels. Finally, among the fusion approaches, one can also consider the STAPLE (Simultaneous Truth And Performance Level Estimation) approach [START_REF] Warfield | Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation[END_REF] which allows the fusion of the segmentations given as input in the framework of an expectation maximization algorithm. This approach also allows to assign a score to the experts associated with the inputs. In this framework the probabilistic estimate of the true segmentation is formed by estimating an optimal combination of the segmentations, weighting each segmentation.

The previously described supervision strategy produce results that are then used to globally weight the output of the different models. These weights can be either fixed, as is the case for the majority voting strategy, or variable (e.g. linear fusion) when some models are known for their expertise on a subset of the database. Unfortunately, none of these strategies consider that each view-specific network may have a local expertise in a portion of the image. Yet, it is also clear that central slices in each of the three orientations (axial, sagittal, coronal) provide better visualization of the continuity of the prostate contour, supporting the idea that expert networks will be more likely to provide a reliable result on these central slices than on slices located at the ends.

To address this problem, we propose a Multi-eXpert Fusion (MXF) framework that performs a new type of pixelscale fusion strategy based on CNNs. Similar to a mixture of experts method, a fusion CNN is trained to produce confidence maps, rather than a single scalar weight per expert. These maps correspond to the local confidence granted to each of the view-specific segmentation networks for a given input. We show that this new fusion framework is robust and can be applied on different types of CNN architectures with simiarticlelar outcomes. We discuss quantitatively and qualitatively the results obtained on a large number of patients based on routine clinical data. These results demonstrate the reliability of the proposed approach and allow a fair comparison with the most recent approaches.

II. MATERIALS AND METHODS

A. Dataset and ground truth

The database consists of 100 TRUS volumes from different patients. Data were collected over a three-year period on patients suspected of having a cancer or followed during their treatment. These volumes were acquired with two different motorized 3D end-fire endorectal probes during routine biopsy series using two different commercial guidance systems (Urostation c and Trinity c from Koelis SAS). The examinations were performed by six urologists from the Grenoble University Hospital. No particular constraints or protocols were imposed to the physicians when performing these procedures. These routine acquisitions in a public health institute and over this three-year period ensure the diversity of patients, practitioners, and equipment that make the database representative of the clinical variability of these procedures.

The images were all segmented by the urologists involved in the biopsy procedure using a semi-automatic 3D algorithm (proprietary to Koelis). This segmentation step is composed of three phases: first the expert selects a bounding box around the organ; then the algorithm proposes a segmentation of the organ; finally, the expert can manually modify the computed shape to improve the segmentation. During the second step, the algorithm fits an atlas model of the prostate having been obtained from MRI images [START_REF] Martin | Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model[END_REF]. During the third phase, the expert can refine the result produced by the algorithm by adding 3D control points on the ultrasound volume. Finally, before being integrated in the database the 100 US volumes were individually screened by another operator in order to reject the examinations for which the quality of the segmentation was not satisfactory. Data collection was declared to the CNIL (French data protection authority) under the reference MR2711140520. As part of the training procedure, the database was randomly divided into two sets: one for training (80 patients) and one for testing (20 patients).

To increase the reliability of the results reported in the following sections, the algorithm was trained with a k-fold validation strategy, with k = 5 to keep the 80/20 ratio. All the results provided in the Section III, correspond to the combination of the 20 test subjects from each fold.

B. MXF framework

The overall objective can be stated as finding the prostate surface being given a 3D TRUS input volume X.

This requires learning a function F so that the segmented volume F (X) is as close as possible to the ground truth segmentation Y .

Determining this function can be related to a class of methods called mixture of experts as described in [START_REF] Svensén | Pattern recognition and machine learning[END_REF]. In a classical mixture of experts framework, F function can be decomposed into n sub-functions named experts E i and a supervision function C i allowing to weight each of the experts' predictions:

Ŷ = n i=1 C i (X)E i (X), with 0 ≤ C i (X) ≤ 1 n i=1 C i (X) = 1 (1)
In the proposed approach, we introduce three experts. Each of them is specialized in the segmentation of 2D US images of a given direction. Each of the three experts (E i ) is a CNN (Section II D) and is trained to segment 2D slices extracted from an input 3D TRUS volume along sagittal, coronal or axial directions (Figure 2). Once the three stacks of 2D slices have been segmented for a given input volume, we can re-stack them to form three segmented volumes.

In a second phase, the initial TRUS volume and these three segmented volumes are fed to a fusion CNN in order to produce the final segmented volume. The originality of the presented approach lies in the fact that the fusion network is trained to produce a tensor of three confidence maps (C i ) used to combine the segmentations from the three experts into a final segmented image ( Ŷ =

3 i=1 C i × E i ).
Unlike traditional supervision methods, the produced confidence maps allow to take into account the experts' local strengths and weaknesses by assigning them a confidence at the pixel-scale for each input slice. Finally, all the segmented slices are pilled up to form the final segmented volume ( Ŷ ) that better estimates the ground truth segmentation.

C. Pre-processing

Prior to the view specific segmentation step, a coarse bounding box is defined around the prostate, using data from the patient's clinical record. This coarse bounding box is the one given by the urologist/radiologist during the routine procedure where data were collected (see Section II A). This information is used to crop the volumes with an additional fixed contextual region of 40 voxels around it. Volumes are then re-sampled to a fixed size (160, 128, 136) which corresponds to an average voxel size of 0.41 mm (±0.05), 0.43 mm (±0.07) and 0.41 mm (±0.06). No other operation is applied. Since the used training base is already large, containing about 10k 2D slices, we did not observe any improvement using data augmentation techniques (such as adding noise or applying transforms. . . ).

D. Expert networks

Several network architectures have been implemented to perform the 2D expert segmentation phase and thus to assess the reliability and flexibility of the overall proposed framework. The first tested architecture is an encoder/decoder network (EncDec). The second one is a U-Net, which is an EncDec with an additional skip connection between the encoding and decoding blocks. The last one is called residual U-Net and can be considered as a U-Net with additional skip connections within each encoding / decoding block [START_REF] Yu | Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d MR images[END_REF].

For the first two architectures (EncDec & U-Net), encoding/decoding blocks are a series of two 3 × 3 convolutional layers each followed by a batch normalization and a ReLU unit. The encoding blocks double the number of features and are connected by 2 × 2 max pooling layers. The decoding blocks halve the numbers of features and are connected by a 2 × 2 up-sampling layers. Finally, a dropout layer has been added after the last encoding block.

The residual U-Net architecture is similar to the U-Net. However, the encoding units are more complex and are composed of two successive sub-units. The first is composed of a series of 1 × 1 convolution, batch normalization and ReLu. The second is made of two blocks of 3 × 3 convolution with batch normalization and ReLu. The output of the encoding units is the combination of the output of these two sub-units leading to the short skip connection. Encoding blocks are connected by 2 × 2 max pooling layers. Decoding blocks include two 3 × 3 convolutions layers with batch normalization and ReLu. These blocks are connected by transposed convolution layers with batch normalization.

All three architectures take as input a single-channel slice tensor. Their output, which has the same size as the input, is given by a 1 × 1 convolution followed by a sigmoid unit.

The Adam optimizer was used with a fixed learning rate set at 1e -4 to optimize a Dice loss function in all scenarios (Equation 2). The Dice loss was chosen since Orlando et al. [START_REF] Orlando | Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images[END_REF] showed that it is an efficient loss for prostate TRUS image segmentation (without significant difference with the Dice / cross-entropy combination).

Loss Dice Y, Ŷ = 1 -2 |Y ∩ Ŷ | |Y | + | Ŷ | (2)

E. Fusion network

Let us remind that the three view-specific experts produce three segmented volumes. To enable the combination of the produced segmentations in the fusion stage, they are re-sliced in the coronal view. The initial volume to be segmented is also re-sliced in the same direction. Thus, the input of the fusion network is a 4-channel tensor composed of a TRUS image in coronal view and three segmented slices obtained at the same location from the three previously view-specific segmented volumes. The network is trained to produce a set of 2D confidence maps C i as a 3-channel tensor (cf. Figure 3). These three confidence maps reflect the trust that the fusion network assigns locally (pixel-scale) to each of the view-specific segmentations in order to produce the final segmentation.

For the fusion network, we have also tested the network architectures described in Section II D. Apart from the adaptation of the number of input and output channels, the only modification was the replacement of the last layer (sigmoid) by a softmax layer. This is required in order to keep the resulting segmentation (sum of the products cf. Equation 1) in the range [0, 1]. Figure 4 illustrates the structure of this fusion network.

As presented in Figure 4, the loss function evaluates the error between the ground truth segmentation Y of the slice and the prediction Ŷ computed using the confidence maps and the expert segmentations. Several combinations between two area-specific cost functions (Dice (Equation 2) and Smooth L1 (Equation 3 with α = 1.0)) and one cost function based on the Hausdorff distance estimation (HD) have been evaluated for this purpose. The HD loss is based on morphological operations as it was originally proposed by Karimi et al. [START_REF] Karimi | Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks[END_REF] (Equation 4). In Equation 4

Loss SL1 Y, Ŷ = |Y -Ŷ | if |Y -Ŷ | > α 1 |α| (Y -Ŷ ) 2 if |Y -Ŷ | ≤ α (3) × × × Inputs
Loss HD Y, Ŷ = 1 |Ω| K k=1 Ω Y -Ŷ 2 k B k 2 (4)

F. Evaluation & implementation

To assess the versatility and reliability of the proposed framework, twelve different scenarios (pair of architecture and loss function) were compared based on the three network architectures and the four loss combinations presented in Sections II D & II E. Within each scenario the same network architecture is used for both the experts and the fusion stage.

In order to give the most comprehensive picture of the quality of the results four 3D metrics are given: two measure the overlapping of segmented surfaces (Dice and Jaccard indexes) and two are related to the distances between contours (average surface distance (ASD) and Hausdorff distance (HD) given in millimeters). All these networks were implemented within the Pytorch framework and the experiments were conducted on a Titan RTX from NVIDIA. All networks were trained separately: first the three expert networks and then the fusion network. They were trained for 100 epochs with a batch size of 50 2D images.

To demonstrate the impact of the fusion strategy, we also evaluated the performances of each expert individually after having re-stacked the segmented 2D slices (see Figure 2).

In order to compare the proposed approach with state-of-the-art methods, we implemented two baseline fusion approaches: the majority voting strategy (MAJ) and simultaneous truth and performance level estimation (STAPLE). The majority voting strategy was implemented as descibed in [START_REF] Man | Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net[END_REF]: at least two of the three experts must give the same answers to label a pixel as part of the prostate. Regarding STAPLE, the expectation-maximization algorithm that considers a collection of segmentations and computes a probabilistic estimate of the true segmentation, we used its SimpleITK implementation.

We also compared the presented method to a 3D global approach using a V-Net. The V-Net [START_REF] Milletari | V-Net: Fully convolutional neural networks for volumetric medical image segmentation[END_REF] is a fully 3D U-Net, it was implemented and configured as suggested by Isensee et al. [START_REF] Isensee | nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[END_REF] (data augmentation/loss). This 3D network was implemented in the Pytorch framework and trained on the same training and test datasets as our proposed approach.

III. RESULTS

A. Performances of the MXF framework

The performances of the twelve scenarios (Section II F) are summarized in Table I. These results show that the MXF framework allows to obtain very accurate and consistent results regardless of the scenario considered. It can be seen that the overlap criteria (Dice and Jaccard) are in all cases very similar or even identical on all the scenarios, as well as the average surface distance metric (ASD). These three criteria allow to characterize the regularity and the consistency of the results produced by the method. The Hausdorff measurement informs about the maximum deviations along the surface and once again one can notice that these maximum errors are relatively low, about 5 mm to 7 mm maximum for all the scenarios.

Table II presents the compared performances of each expert to MXF best results.

Finally, although our implementation is focused on method quality assessment and is therefore not optimized to run on a clinical platform, the validation step takes less than a minute to segment all patients in the test database. This computation time includes reading, processing and writing the data. These elements ensure that the proposed approach can run almost instantly on the ultrasound platform once the code is optimized for it. Figure ?? illustrates the process of building the segmentation of a slice at the end of the MXF pipeline. It shows the type of errors that the majority voting strategy will tend to produce, especially in the inferior region of the prostate (see Figure I) where the experts often disagree as in the presented example. One can see that the proposed method is able to provide a better consensus. The figure also introduces C RGB , a synthetic representation used in Figure 5 230 to visualize the three confidence maps associated with the three experts as a single RGB image.

× × × + + = = = = E 1 E 2 E 3 C 1 C 2 C 3 C RGB Ŷ Contours MXF GT MAJ
Figure 5 gives, for several slices, typical instances of the confidence maps produced by the fusion network and the segmentation obtained from them, for the U-net implementation. These examples show that the contours regularity is much better with the fusion network than with the majority voting strategy, although no regularity constraint was applied. In addition, the fusion network generally produces a single, full and connected element with a rather smooth boundary. This figure also shows that, even if the overall results look alike (cf. Table I), the SL1 loss helps to produce smoother confidence maps compared to the Dice loss. This may be related to the fact that this loss function is smoother and explain the results slightly in favor of the SL1 loss (see Section III A). Figure 6 summarizes the results obtained with the MXF and majority voting fusion strategies (best scenario) on three regions of the gland (from the base to the apex). The regions are computed to represent equivalent sub-volumes of the prostate. It highlights the importance of the fusion network, especially in the prostate regions considered more difficult to segment (base and apex).

C. Comparison with the state of the art

As explained in Section II F, we have implemented the majority voting strategy, the STAPLE strategy and a V-Net to compare our method directly to them. The results obtained with these three approaches are given in Table III.

245

As it can be seen, the proposed method significantly outperforms the baseline majority voting strategy and STAPLE Our results can also be favorably compared to the state of the art. For instance, Wang et al. [START_REF] Wang | Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound[END_REF] obtained an average Dice of 0.90 ± 0.02 (Jaccard 0.82 ± 0.04) and an ASD 1.16 ± 0.7 mm in the best cases on a dataset composed of 40 patients (with a four fold cross validation strategy). Orlando et al. [START_REF] Orlando | Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images[END_REF] have obtained an overall absolute median Dice of 0.94 and an absolute median ASD of 0.89 mm on their test dataset of 20 patients. The corresponding values of our method for the best scenario (U-Net) are 0.93 and 0.84 mm respectively. Finally, Girum et al. [START_REF] Girum | A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy[END_REF] proposed a 3D segmentation method based on 2D segmentation but without fusion stage and have obtained and average Dice of 0.88 ± 0.02 and an average HD of 8.37 ± 2.93 mm on 14 patients.

IV. DISCUSSION AND CONCLUSION

Our goal is to provide an efficient, reliable and reproducible 3D segmentation method to assist the physician during a clinical examination such as systematic biopsies. The optimal segmentation method should both improve the quality of the medical procedure and limit patient discomfort by shortening intervention time. To ensure the effectiveness of the proposed method, it is necessary to use data that suitably represent the clinical complexity.

We proposed MXF, a novel deep learning-based ensemble learning framework, to segment the prostate gland in 3D TRUS images. The proposed framework consists of two stages. In the first one, three view-specific CNNs, called experts, are trained to segment 2D slices extracted from an input 3D TRUS volume. The second stage is based on a fusion CNN. The three view-specific CNNs are fed by axial, coronal, and sagittal slices, respectively. The fusion network is fed by axial slices re-sliced from the three segmented volumes reconstructed in the first stage. This fusion CNN is trained to produce three confidence maps used to merge the outputs of these view-specific CNNs at the pixel-scale. Unlike the existing strategies, which reconstruct the volume with fixed weights such as majority voting [START_REF] Man | Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net[END_REF] or without an additional fusion process [START_REF] Orlando | Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images[END_REF][START_REF] Girum | A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy[END_REF], the presented approach assumes that confidence can be granted at pixel-scale to each of the specific segmentation networks for a given input. This feature is the key contribution of the proposed approach and has not been proposed in an image segmentation framework to the best of our knowledge.

Our database includes 100 patients issued from biopsy examinations routinely performed by the urologists of the University Hospital of Grenoble. The use of data from various machines, different patients and annotated by several clinicians contribute to the robustness of the proposed method. Nevertheless one limitation of the proposed work is the use of a database acquired from a single hospital. In addition, although six experts worked on the segmentation of the US volumes and another operator screened the data to exclude those which did not meet the required quality, each patient volume was segmented by a single urologist. Performing multi-centric evaluation and having multi-operator segmentations for each patient would allow us to better evaluate the inter-and intra-observer variability and increase the robustness of proposed approach.

The evaluation demonstrated that the proposed fusion network approach was able to capture and preserve the underlying structures of the gland, especially in the inferior and superior regions. This ability is very important as these areas, considered to be the most difficult to segment by doctors, are also major parts of the peripheral zone known to be the preferred area for tumor localization. By evaluating scenarios composed of different architecture/loss function pairs, we have also demonstrated the positive contribution and flexibility of our approach and in particular of the fusion stage. Indeed, in each scenario the proposed approach gave better results than the majority voting strategy, STAPLE algorithm and V-net. Although the results obtained with the Dice and SL1 loss functions were not significantly different in quality, it was apparent upon visual inspection that the confidence maps generated by the SL1 loss function appeared smoother.

To put the results obtained by the proposed framework into perspective, the best performances of the view-specific networks (Dice = 0.89, Jaccard = 0.81, ASD = 1.39 mm, HD = 10.01 mm) of the majority voting strategy (0.88, 0.82, 1.34 mm, 6.35 mm) and the STAPLE (0.88, 0.82, 1.35 mm, 6.46 mm), are significantly poorer than the results obtained with the MXF fusion strategy (0.93, 0.86, 0.83 mm, 5.49 mm), especially in terms of ASD and maximum error (Hausdorff criterion). One of the possible reasons behind these results is that the fusion network learns what can be considered as an a priori, namely that the slices located in the middle of a slicing axis are easier to segment than the ones at the extremity. The expert networks results reflect these difficulties, that are associated with errors, more or less important, located along the edge of the prostate in different quadrants.

Compared to a 3D approach, like V-Net, the proposed method also performs better, even with the use of data augmentation and specific configurations to set up the 3D CNN and its training environment. The results obtained from our database, generally larger than those found in the literature, can be compared favorably with the other methods from the literature.

From the point of view of calculation time, even if the training of the different networks takes few hours, the application to a new patient is almost instantaneous. This, together with the good results obtained by the method, suggest a possible transition to routine clinical application. Future work will focus on evaluating an even larger database from multiple hospitals and will also seek to assess the impact of inter-and intra-observer variability in expert segmentation on training quality and generalization quality. Finally, the extension of MXF to the segmentation of other non-isotropic modalities (such as MRI) could be an interesting addition to this work.

Figure 2 .

 2 Figure 2. Overall architecture of the MXF framework.

  , k denote k successive erosion with the kernel B = Adam optimizer was used with a fixed learning rate set to 1e -4 to optimize the losses.

Figure 3 .

 3 Figure 3. Details of the fusion stage.

Figure 4 .

 4 Figure 4. U-Net based version of the fusion network.

Figure 5 .

 5 Figure 5. Construction process of the Ŷ segmentation from the experts' outputs Ei and the confidence maps Ci provided by the MXF framework, illustrated on a sample slice located close to the apex. Color intensity corresponds to the level of confidence. MXF=our framework; MAJ=majority voting; GT=ground truth

Figure 6 .

 6 Figure 6. Visual evaluation of MXF results obtained for the U-Net scenario. The three confidence maps Ci are displayed as a single RGB image CRGB, each of the three confidence maps being associated with a color channel (where R, G and B correspond to sagittal, axial and coronal views respectively). The contours on the TRUS image correspond to the ground truth (purple), MXF (turquoise) and majority voting (orange). The figure shows 2D slices from the inferior region (upper row) to the superior region (lower row) of the prostate.

Figure 7 .

 7 Figure 7. Sectoral evaluation (inferior, midgland, superior as in Figure I) for the MXF framework (best scenario U-Net) and the majority vote strategy. For each sector, for each evaluation criterion, the average value computed on the test dataset is given.

Table I .

 I MXF framework performances for the twelve evaluated scenarios (see Section II F).

	Architecture	Encoder/Decoder	Residual U-Net	U-Net
	X Criterion X X X X X	X Loss X X	Dice Dice+HD	Dice Dice+HD	Dice Dice+HD
	Dice	mean	0.92	0.92	0.92	0.92	0.93	0.92
		std	0.04	0.04	0.04	0.04	0.04	0.04
	Jaccard	mean	0.86	0.85	0.85	0.86	0.85	0.86
		std	0.07	0.06	0.07	0.07	0.07	0.07
	ASD	mean	0.85	0.86	0.85	0.86	0.84	0.83
		std	0.50	0.52	0.47	0.48	0.51	0.44
	HD	mean	7.41	7.11	7.41	7.47	6.38	6.15
		std	2.12	2.01	4.87	2.46	3.11	2.87
	X Criterion X X X X X	X Loss X X	SL1 SL1+HD	SL1 SL1+HD	SL1 SL1+HD
	Dice	mean	0.92	0.92	0.92	0.92	0.93	0.92
		std	0.04	0.04	0.04	0.04	0.04	0.04
	Jaccard	mean	0.85	0.85	0.85	0.86	0.86	0.86
		std	0.07	0.07	0.06	0.07	0.06	0.07
	ASD	mean	0.86	0.84	0.85	0.85	0.83	0.83
		std	0.51	0.49	0.59	0.53	0.41	0.48
	HD	mean	7.36	7.83	6.93	6.79	5.48	5.42
		std	2.81	2.91	3.24	3.11	2.66	2.58

Table II .

 II MXF framework results compared to expert results alone within the best scenario (U-Net).

	`````````C riterion	Method	MXF	Axial	Coronal	Sagittal
	Dice	mean	0.93	0.87	0.88	0.89
		std	0.04	0.03	0.04	0.03
	Jaccard	mean	0.86	0.79	0.81	0.81
		std	0.06	0.04	0.06	0.05
	ASD	mean	0.83	1.37	1.34	1.39
		std	0.41	0.58	0.52	0.57
	HD	mean	5.48	10.14	11.12	10.01
		std	2.66	4.12	3.41	3.76

Table III .

 III Comparison of MXF (best scenario U-Net) to a fusion strategy using the majority voting or STAPLE and to a V-Net 3D segmentation (see Section III C).

	`````````C riterion	Method	MXF	MAJ STAPLE	V-Net
	Dice	mean	0.93	0.88	0.88	0.89
		std	0.04	0.04	0.04	0.04
	Jaccard	mean	0.86	0.82	0.82	0.81
		std	0.06	0.06	0.05	0.06
	ASD	mean	0.83	1.34	1.35	1.41
		std	0.41	0.49	0.49	0.39
	HD	mean	5.48	6.35	6.46	6.54
		std	2.66	3.02	3.25	2.17
	in all scenarios. The results between proposed, majority voting and STAPLE strategies were compared statistically:
	Shapiro tests within each strategy to demonstrate the normality and Wilcoxon tests to demonstrate significant differ-
	ence.

ACKNOWLEDGMENTS

This work was partly supported by Auvergne Rhône-Alpes region (project ProNavIA) and by the French "Agence Nationale de la Recherche", Investissement d'Avenir program (grants MIAI@Grenoble-Alpes under reference ANR-19-P3IA-0003 and CAMI Labex under reference ANR-11-LABX-0004). The authors also want to thank the urologists from Grenoble University Hospital for providing the data.

The authors have no conflicts to disclose.


Vincent Le Moigne
email: vincent.le-moigne@uvsq.fr

Anne-Laure Roux

Hélène Mahoudo

Gaëtan Christien

Agnès Ferroni

Oana Dumitrescu

Gérard Lina

Jean-Philippe Bouchara

Patrick Plésiat

Jean- Louis Gaillard

Stéphane Canaan

Geneviève Héry-Arnaud

Jean-Louis Herrmann
email: louis.herrmann@aphp.fr

SEROLOGICAL BIOMARKERS FOR THE DIAGNOSIS OF MYCOBACTERIUM ABSCESSUS INFECTIONS IN CYSTIC FIBROSIS PATIENTS

Keywords: non-tuberculous mycobacteria, cystic fibrosis, serology, serodiagnosis, ELISA

Background: Culture conditions sometimes make it difficult to detect non-tuberculous mycobacteria (NTM), particularly Mycobacterium abscessus, an emerging cystic fibrosis (CF) pathogen. The diagnosis of NTM positive cases not detected by classical culture methods might benefit from the development of a serological assay.

Methods: As part of a diagnostic accuracy study, a total of 173 sera CF-patients, including 33 patients with M. abscessus positive cultures, and 31 non-CF healthy controls (HC) were evaluated. Four M. abscessus antigens were used separately, comprising two surface extracts (Interphase (INP) and a TLR2 positive extract (TLR2eF)) and two recombinant proteins (rMAB_2545c and rMAB_0555 also known as the phospholipase C (rPLC)).

Results: TLR2eF and rPLC were the most efficient antigens to discriminate NTM-culture positive CF-patients from NTM-culture negative CF-patients. The best clinical values were obtained for the detection of M. abscessus-culture positive CF-patients; with sensitivities for the TLR2eF and rPLC of 81.2% (95% CI:65.7-92.3%) and 87.9% (95% CI:71.9-95.6%) respectively, and specificities of 88.9% (95% CI:85.3-94.8%) and 84.8% (95% CI:80.6-91.5%) respectively. When considering as positive all sera, giving a positive response in at least one of the two tests, and, as negative, all sera negative for both tests, we obtained a sensitivity of 93.9% and a specificity of 80.7% for the detection of M. abscessus-culture positive CF-patients.

Conclusion:

High antibody titers against TLR2eF and rPLC were obtained in M. abscessusculture positive CF-patients, allowing us to consider these serological markers as potential tools in the detection of CF-patients infected with M. abscessus.

INTRODUCTION

Non-tuberculous mycobacterial pulmonary infections remain frequent and, depending on the mycobacterium, severe for patients with cystic fibrosis (CF). Nineteen epidemiological studies have been published to date, representing a total number of 23,418 CF-patients, 6% of whom were found to be infected with non-tuberculous mycobacteria (NTM) [START_REF] Quitterie | Risk factors for nontuberculous mycobacterial isolation in patients with cystic fibrosis: A meta-analysis[END_REF]. In fact, numbers vary from 3.3% and up to 13% from one study to another [START_REF] Martiniano | Nontuberculous mycobacterial infections in cystic fibrosis[END_REF][START_REF] Nick | Nontuberculous mycobacterial infections in cystic fibrosis: to treat or not to treat?[END_REF][START_REF] Levy | Multicenter cross-sectional study of nontuberculous mycobacterial infections among cystic fibrosis patients, Israel[END_REF][START_REF] Binder | Epidemiology of nontuberculous mycobacterial infections and associated chronic macrolide use among persons with cystic fibrosis[END_REF][START_REF] Roux | Multicenter study of prevalence of nontuberculous mycobacteria in patients with cystic fibrosis in France[END_REF][START_REF] Richards | Nontuberculous mycobacteria in cystic fibrosis[END_REF][START_REF] Gardner | Epidemiology of nontuberculous mycobacteria infection in children and young people with cystic fibrosis: Analysis of UK cystic fibrosis registry[END_REF][START_REF] Hatziagorou | Electronic address: ECFS-Patient[END_REF]. The risk factors for acquisition of NTM include concurrent chronic infection due to another infectious agent (Staphylococcus aureus, Aspergillus fumigatus, Stenotrophomonas maltophilia), older CFpatients, the use of oral corticosteroids and intravenous broad spectrum antimicrobial therapy [START_REF] Quitterie | Risk factors for nontuberculous mycobacterial isolation in patients with cystic fibrosis: A meta-analysis[END_REF][START_REF] Catherinot | Mycobacterium avium and Mycobacterium abscessus complex target distinct cystic fibrosis patient subpopulations[END_REF]. The main NTM isolated from CF-patients according to recently defined diagnostic criteria [START_REF] Griffith | An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases[END_REF][START_REF] Floto | US Cystic Fibrosis Foundation and European Cystic Fibrosis Society consensus recommendations for the management of non-tuberculous mycobacteria in individuals with cystic fibrosis: executive summary[END_REF] are Mycobacterium avium and Mycobacterium intracellulare, which belong to the M. avium complex, and Mycobacterium abscessus sensu lato [START_REF] Binder | Epidemiology of nontuberculous mycobacterial infections and associated chronic macrolide use among persons with cystic fibrosis[END_REF][START_REF] Roux | Multicenter study of prevalence of nontuberculous mycobacteria in patients with cystic fibrosis in France[END_REF][START_REF] Floto | US Cystic Fibrosis Foundation and European Cystic Fibrosis Society consensus recommendations for the management of non-tuberculous mycobacteria in individuals with cystic fibrosis: executive summary[END_REF][START_REF] Olivier | Nontuberculous mycobacteria. I: multicenter prevalence study in cystic fibrosis[END_REF][START_REF] Azar | Distribution and outcomes of infection of Mycobacterium avium complex species in cystic fibrosis[END_REF][START_REF] Vande Weygaerde | Clinical relevance of pulmonary non-tuberculous mycobacterial isolates in three reference centres in Belgium: a multicentre retrospective analysis[END_REF].

What has become clear is that almost 50% of CF-patients with an NTM isolated from their respiratory specimens comply with the defined diagnostic criteria regarding NTM pulmonary disease [START_REF] Griffith | An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases[END_REF]. In addition, the pathogenicity of the isolated NTMs must now be considered [START_REF] Azar | Distribution and outcomes of infection of Mycobacterium avium complex species in cystic fibrosis[END_REF][START_REF] Aksamit | Nontuberculous mycobacterial (NTM) lung disease: the top ten essentials[END_REF]. Their clinical relevance varies according to the NTM species but also to radiological findings on chest CT (computerized tomography) and Acid-Fast Bacilli (AFB) staining, leading to the observation that only certain NTMs are or will be consistently associated with a pulmonary pathology [START_REF] Vande Weygaerde | Clinical relevance of pulmonary non-tuberculous mycobacterial isolates in three reference centres in Belgium: a multicentre retrospective analysis[END_REF], while meeting previously defined criteria [START_REF] Griffith | An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases[END_REF]. Finally, the impact of NTM lung infection on ventilatory function in CF-patients, and even on mortality in any population outside CF, has only recently been evaluated. Nevertheless, there is now strong evidence that, according to the causative species, NTM lung infections reduce the lung capacity of CF-patients, and that mortality of CF infected individuals at five years, is significantly higher, with or without co-morbidities, compared to the mortality calculated for the general population [START_REF] Qvist | Epidemiology of nontuberculous mycobacteria among patients with cystic fibrosis in Scandinavia[END_REF][START_REF] Mourad | Reduction in expected survival associated with nontuberculous mycobacterial pulmonary disease[END_REF]. Therefore, a reliable and accurate microbiological diagnosis is necessary. Unfortunately, a microbiological diagnosis relies on inoculation of respiratory specimens onto enriched media specific to mycobacteria, which may be rapidly contaminated by other CF pathogens, notably Pseudomonas aeruginosa or Staphylococcus aureus, and therefore unusable despite the possibility of a second step of decontamination of the respiratory specimen [START_REF] Floto | US Cystic Fibrosis Foundation and European Cystic Fibrosis Society consensus recommendations for the management of non-tuberculous mycobacteria in individuals with cystic fibrosis: executive summary[END_REF]. Recent work on a new medium makes it possible to avoid the decontamination step of the respiratory sample, with a certain increase in the sensitivity of the culture [START_REF] Perry | A decade of development of chromogenic culture media for clinical microbiology in an era of molecular diagnostics[END_REF]. However, this medium is still largely a more favorable medium for the growth of rapid-growing NTM (RGM) compared to slow-growing NTM (SGM), as shown recently, with only a 50% recovery for M. avium and M. intracellulare [START_REF] Scohy | Comparison of the RGM medium and the mycobacterial growth indicator tube automated system for isolation of non-tuberculous mycobacteria from sputum samples of cystic fibrosis patients in Belgium[END_REF].

An indirect diagnosis, based on the host's immune response during mycobacterial infection through measurement of antibody production, has not yet demonstrated its effectiveness, particularly in the context of tuberculosis [START_REF] Steingart | Commercial serological tests for the diagnosis of active pulmonary and extrapulmonary tuberculosis: an updated systematic review and meta-analysis[END_REF]. In the setting of NTM infections in CFpatients, data are scarce, with clinical values difficult to interpret [START_REF] Ravnholt | Antibody response against Mycobacterium avium complex in cystic fibrosis patients measured by a novel IgG ELISA test[END_REF][START_REF] De | Urine lipoarabinomannan as a marker for low-risk of NTM infection in the CF airway[END_REF]. A few studies focused on the characterization of M. abscessus lung infections by ELISA assays [START_REF] Ferroni | Measurement of immunoglobulin G against Mycobacterial antigen A60 in patients with cystic fibrosis and lung infection due to Mycobacterium abscessus[END_REF][START_REF] Qvist | Serodiagnosis of Mycobacterium abscessus complex infection in cystic fibrosis[END_REF].

Nevertheless, when considering the common nature of the antigens evaluated in many NTMs, it seemed essential to us to develop a serological assay that would immediately detect CFpatients with the presence of antibodies against NTM, differentiating a current or past NTM infectious process. The idea was then to investigate in more detail those CF-patients considered NTM positive using the culture techniques or even molecular methods at our disposal. We took advantage of two antigenic preparations, previously described [START_REF] Bakala | Mycobacterium abscessus phospholipase C expression is induced during coculture within amoebae and enhances M. abscessus virulence in mice[END_REF][START_REF] Moigne | A TLR2-activating fraction from Mycobacterium abscessus Rough variant demonstrates vaccine and diagnostic potential[END_REF], and for which we demonstrated, in the course of this work, their value as serological diagnostic tools for detecting NTM-CF infected patients, and more specifically CF patients with positive M. abscessus cultures.

MATERIAL and METHODS

Patients and antigens

Serum samples for antibody determination collected from 173 CF-patients (6) and 31 healthy controls (HC) were stored at -20°C for further investigation. Recombinant MAB_0555 (rPLC) was produced and purified as previously described [START_REF] Moigne | Bacterial phospholipases C as vaccine candidate antigens against cystic fibrosis respiratory pathogens: the Mycobacterium abscessus model[END_REF]. MAB_2545c was cloned in the plasmid pMyC and the recombinant protein, rMAB_2545c, was expressed and purified as described [START_REF] Moigne | Bacterial phospholipases C as vaccine candidate antigens against cystic fibrosis respiratory pathogens: the Mycobacterium abscessus model[END_REF]. Mycobacterial Interphase (INP) and TLR2eF preparations were obtained as described [START_REF] Roux | Overexpression of proinflammatory TLR-2-signalling lipoproteins in hypervirulent mycobacterial variants[END_REF][START_REF] Moigne | A TLR2-activating fraction from Mycobacterium abscessus Rough variant demonstrates vaccine and diagnostic potential[END_REF].

ELISA assays

IgG antibodies against INP, TLR2eF, rMAB_2545c and rPLC were determined by ELISA as previously described [START_REF] Bakala | Mycobacterium abscessus phospholipase C expression is induced during coculture within amoebae and enhances M. abscessus virulence in mice[END_REF]. Briefly, plates were coated overnight at 4°C with 1 µg/ml of each antigen in 100 µl of carbonate-bicarbonate buffer (0.1 M, pH 9.6), except for INP that was diluted and coated in water/ethanol (50/50, v/v). Plates were then washed twice with PBS and blocked by incubation for one hour at 37°C with PBS containing 1% bovine serum albumin (PBS-BSA). Sera were added at a 1/400 dilution in PBS-Tween 20 (0.05% v/v) (PBS-T) containing 0.5% BSA (PBS-T-BSA). After incubation for 1 h 30 at 37°C followed by four washes with PBS-T, alkaline phosphatase-conjugated goat anti-human IgG or goat antihuman IgA (Southern biotechnology, Birmingham, USA) diluted in PBS-T-BSA was added.

Plates were then incubated for 1 h 30 at 37°C. After four additional washes, 100 µl of 1 mg/ml of p-nitrophenylphosphate (Sigma, Saint Quentin Fallavier, France) in diethanolamine buffer (pH 9.8) was added, and plates were incubated in the dark at room temperature for 2 h.

Plates were then read spectrophotometrically at 405 nm with a Bio-Rad PR 3100 TSC instrument (Bio-Rad France, Marnes-la-Coquette, France). The ELISA against the Mycobacterium avium subsp. paratuberculosis lipopentapeptide (L5P) was performed as previously described [START_REF] Biet | Lipopentapeptide induces a strong host humoral response and distinguishes Mycobacterium avium subsp. paratuberculosis from M. avium subsp. avium[END_REF]. per series. Each serum sample was evaluated on at least two series. A strong positive and a weak positive serum were systematically added to each plate to establish inter-plate and interday variability. OD mean ± standard deviation was calculated for each patient group.

Sensitivity and specificity of the ELISA assays were assessed by calculating the area under the receiver operating characteristic curve (AUC). The test positivity thresholds were determined using this receiver operating characteristics (ROC) curves [START_REF] Metz | Basic principles of ROC analysis[END_REF]. Corresponding ROC curves were presented in supplementary Figures 1A to 1D and in supplementary Figures 2A to 2D. The closest point to the upper left side corner was used to determine the cut-off value.

Data analysis.

We used Student's t test for all analyses using GraphPad Prism 6.0 software (GraphPad Software, La Jolla, CA, USA) and considered a 2-sided P of < 0.05 to be statistically significant (ns = non-significant, *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001).

RESULTS

Distribution of serological samples according to the pathogens isolated in culture

A total of 204 sera from 173 patients with CF and 31 HC were studied (Figure 1). All sera were obtained prospectively during the 2004 survey of atypical mycobacteria infection in CF patients in France [START_REF] Roux | Multicenter study of prevalence of nontuberculous mycobacteria in patients with cystic fibrosis in France[END_REF]. Depending on the results of sputum cultures, several groups of patients were distinguished according to strain of mycobacteria, the bacteria and/or the fungi isolated (Figure 1). A first group included 33 CF-patients with a mycobacterium isolated from their sputum and belonging to the M. abscessus complex (Mabs, 28 M. abscessus subspecies abscessus, 1 subspecies bolletii and 4 subspecies massiliense). A second group included 21 CF-patients with mycobacteria belonging to the M. avium complex (MAC, 14 M. avium, 2 Mycobacterium chimaera and 5 M. intracellularea subgroup named Mint). A third group (NTMa) included 5 CF patients with the following mycobacteria isolated from their sputa: 2 Mycobacterium chelonae, 1 Mycobacterium poriferae, 1 Mycobacterium lentiflavum and 1 Mycobacterium gordonae. All together, we had an NTM-culture positive group of 59 CFpatients [START_REF] Griffith | An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases[END_REF][START_REF] Floto | US Cystic Fibrosis Foundation and European Cystic Fibrosis Society consensus recommendations for the management of non-tuberculous mycobacteria in individuals with cystic fibrosis: executive summary[END_REF]. Three other groups of CF-patients were defined, without mycobacteria recovered from sputum samples, including 35 CF-patients with P. aeruginosa detected by culture (PA), 51 CF-patients without NTM nor P. aeruginosa, and 28 CF-patients with fungi only (Figure 1). Thus, we had a total of 114 CF-patients with no proven NTM infection but colonized or infected by other microorganisms. These different groups were therefore compared to each other in terms of antibody response to the different antigens tested and compared to the control group of 31 HC.

Diagnostic value of the Interphase, TLR2eF, rMAB_2545c and rPLC IgG assays when comparing NTM-infected vs. NTM-non infected CF-patients

We previously characterized a complex M. abscessus fraction from its cell surface called Interphase (INP) that reproduced by itself the TLR2-dependent hyper-proinflammatory response observed with the R morphotype [START_REF] Roux | Overexpression of proinflammatory TLR-2-signalling lipoproteins in hypervirulent mycobacterial variants[END_REF]. In this fraction, lipoproteins have been found to be the main TLR2 agonists. To obtain larger amounts of these lipoproteins, we modified the protocol and purified another fraction from M. abscessus cell wall, named TLR2eF [START_REF] Moigne | A TLR2-activating fraction from Mycobacterium abscessus Rough variant demonstrates vaccine and diagnostic potential[END_REF].

Two recombinant proteins, the previously described PLC [START_REF] Bakala | Mycobacterium abscessus phospholipase C expression is induced during coculture within amoebae and enhances M. abscessus virulence in mice[END_REF] and MAB_2545c, present in large quantities in the INP fraction [START_REF] Roux | Overexpression of proinflammatory TLR-2-signalling lipoproteins in hypervirulent mycobacterial variants[END_REF], were also investigated in our study. The four antigenic extracts or recombinant proteins were then used as antigens separately in our ELISA assay.

IgG antibody titers against INP in NTM-infected group (59 CF-patients) vs. non-NTM infected group (114 CF-patients + 31 HC) allowed us to discriminate CF-patients with or without an NTM (p < 0.0001) (Figure 2A). The AUC of 0.655 allowed us to determine a cutoff value of 0.2479 (not shown). Sensitivity and specificity of INP ELISA for detecting NTMculture positive individuals were 49.2% (95% confidence interval [CI]: 35.9-62.5%) and 82.2% (95 % CI: 75.0-88.0%), respectively.

The IgG antibody titers against TLR2eF in NTM-infected group vs. non-NTM infected and control groups separate even more significantly both groups (p < 0.0001) (Figure 2B). Indeed, AUC was 0.798 with a cut-off value of 2.023 (not shown). Moreover, sensitivity and specificity of the TLR2eF ELISA for detecting NTM-culture positive individuals were 61% (95% CI: 47.73-74.59%) and 91.7% (95 % CI: 86.08-95.68%), respectively.

Results for the use recombinant proteins as specific antigens were protein dependent. IgG antibody titers against rMAB_2545c in NTM-culture positive group vs. non-NTM and control groups did not lead to a differentiation between both groups (p = 0.54) (Figure 2C). In contrast, IgG antibody titers against rPLC clearly differentiated both groups (p < 0.0001) (Figure 2D). AUC was 0.779 and it allowed the determination of a cut-off value of 2.039 (not shown). Sensitivity and specificity of the rPLC ELISA for the detection of NTM-culture positive CF-patients were closed to the results obtained with TLR2eF, 64.4% (95% CI: 50.9-76.4%) and 86.9% (95 % CI: 80.3-91.9%), respectively. Positive and negative predictive values, likelihood ratios and accuracy for all antigens are given in Supplementary Tables 1A to 1D. They were calculated on the basis of a prevalence of 3.6% according to the results obtained in the previous prevalence study [START_REF] Roux | Multicenter study of prevalence of nontuberculous mycobacteria in patients with cystic fibrosis in France[END_REF]. The more interesting scores was obtained with TLR2eF as antigen.

Diagnostic value of the Interphase, TLR2eF, rMAB_2545c and rPLC IgG assays when comparing mycobacterial species.

Our ELISA assay using INP as antigen clearly differentiated CF-patients with M. abscessus (Mabs) positive culture from the other NTM-culture positive patient group (p = 0.034), as well as from CF-patients with no-NTM (p < 0.0001), and from HC (p < 0.0001) (Figure 3A).

Comparison with the CF-patient group with M. avium complex (Mac) positive culture also revealed a significant difference (p = 0.005). However, when considering only CF-patients with M. intracellulare (Mint) positive cultures (supplementary Figure 3A), we no longer observed a significant difference in mean OD values with Mabs-culture positive CF-patients.

The mean OD was lower in the Mint group than in the Mabs group, but the difference was not significant (p = 0.29) (supplementary Figure 3A). Sensitivity and specificity for detecting Mabs-culture positive CF-patients reached 78.8% (95% CI: 54.5-86.7%) and 81.3% (95 % CI:

75.4-87.4%), respectively. In comparison, sensitivity and specificity for detecting Mac-culture positive CF-patients were 19% and 81.3% respectively (supplementary Table 2A).

Similarly, the OD values obtained with the TLR2eF antigen also significantly separate the Mabs-culture positive CF-patients from the other patient groups (p < 0.0001), except for the Mint-culture positive CF-patient group (p = 0.84), when segregated from the Mac group (Figure 3B). In this case, sensitivity, and specificity for the detection of Mabs-culture positive CF-patients were 82% (95% CI: 65.7-92.3%) and 90% (95 % CI: 85.3-94.8%) respectively.

By comparison, sensitivity, and specificity for detecting Mac-culture positive CF-patients were 23.8% and 90.6%, respectively (supplementary Table 2B). Of note, Mint-culture positive CF-patients were as responsive as Mabs-culture positive CF-patients, and more responsive than M. avium-culture positive CF-patients (supplementary Figure 3B).

Results obtained with rMAB_2545c were disappointing since it was not possible to discriminate between groups using this antigen (Figure 3C). Sensitivity and specificity for detecting Mabs-culture positive CF-patients were 63.7% (95% CI: 43.7-78.9%) and 76.6%

(95 % CI: 70.4-83.9%) respectively. By comparison, sensitivity, and specificity for detecting Mac-culture positive CF-patients were 19% and 76%, respectively (supplementary Table 2C).

Again, Mint-culture positive CF-patients showed a similar antibody response towards MAB_2545c as Mabs-culture positive CF-patients, and a higher response compared to Macculture positive CF-patients (supplementary Figure 3C).

Finally, the use of rPLC as antigen clearly showed significant differences between groups, with a clear separation between Mabs-culture positive CF-patients and all other patient groups (p < 0.0001). Sensitivity and specificity for the detection of Mabs-culture positive CF-patients were 88% (95% CI: 71.9-95.6%) and 84.5% (95 % CI: 80.6-91.5%) respectively. By comparison, sensitivity, and specificity for detecting Mac-culture positive CF-patients were 33.3% and 87.3% respectively (supplementary Table 2C). As with the two previous antigens, TLR2eF and rMAB_2545C, the OD values obtained for Mint-culture positive CF-patients were almost identical to those obtained for the Mabs-culture positive CF-patients (p = 0.95), and higher than those obtained for Mac-culture positive CF-patients (supplementary Figure 3D). Positive and negative predictive values, likelihood ratios and accuracy for all antigens are given in Supplementary Tables 3A to 3D, the more interesting results being also here obtained with TLR2eF antigen.

Diagnostic value of combined results obtained with the TLR2eF extract and rPLC

ELISA assays

When we review the data obtained individually, antigen by antigen, we found TLR2eF and rPLC, making it possible to separate CF-patients with NTM and/or M. abscessus positive culture from the other patient groups, regardless of any other bacteria or fungi isolated from their respiratory secretions. The characterization of these two antigens may be not surprising given their recently demonstrated vaccine properties [START_REF] Moigne | A TLR2-activating fraction from Mycobacterium abscessus Rough variant demonstrates vaccine and diagnostic potential[END_REF][START_REF] Moigne | Bacterial phospholipases C as vaccine candidate antigens against cystic fibrosis respiratory pathogens: the Mycobacterium abscessus model[END_REF]. As part of an "or" strategy, we therefore compiled results for each antigen, and categorized samples as positive when at least one of the two tests was positive, and as negative when the results were negative for both tests. Data were then compared to culture results i.e., NTM-positive culture and Mabspositive culture (Tables 1A andB). Sensitivity and specificity for the detection of CF-patients with NTM positive culture were 66.1% and 82.8% respectively (Table 1A), and were 94% and 80.7%, respectively, for the detection of CF-patients with M. abscessus positive culture (Table 1B). Positive and negative predictive values, likelihood ratios and accuracy for these two Tables are presented in Tables 1C and1D, with a NPV of 99.7% when sera from Mabspositive culture were compared to Mabs-negative culture.

DISCUSSION

The microbiological diagnosis of NTM lung infections in patients with CF remains challenging [START_REF] Delogu | Mycobacterium species[END_REF]. Sputum samples received in the laboratory often contain several other pathogenic bacteria (S. aureus, P. aeruginosa, Stenotrophomonas maltophilia, Burkholderia cepacia complex...), which grow much faster than mycobacteria. This requires a treatment step called decontamination-concentration, with a 4% sodium hydroxide solution, similar to that performed on sputum received for the diagnosis of Mycobacterium tuberculosis pulmonary infection [START_REF] Delogu | Mycobacterium species[END_REF]. The purpose of this step is to eliminate these pathogenic bacteria, as well as the saprophytic associated microbial flora. However, this decontamination is not insignificant for mycobacteria, especially the RGM, leading to the loss of almost 90% of the mycobacteria present in the sample, without eliminating all other pathogenic bacteria such as P. aeruginosa. Therefore, a second decontamination step is often required, with oxalic acid, even more drastic for mycobacteria [START_REF] Ferroni | Value of the chlorhexidine decontamination method for recovery of nontuberculous mycobacteria from sputum samples of patients with cystic fibrosis[END_REF]. The result, experienced daily in the mycobacteriology laboratory, is therefore either a culture contaminated by one of the associated pathogenic bacteria such as P. aeruginosa, or the absence of mycobacterial growth following successive sputum decontaminations, leading to a low clinical yield for the culture [START_REF] Ferroni | Value of the chlorhexidine decontamination method for recovery of nontuberculous mycobacteria from sputum samples of patients with cystic fibrosis[END_REF]. In addition, many patients such as the youngest patients with CF cannot produce sufficient sputum samples making it challenging to identify mycobacteria, as recommended by recent recommendations [START_REF] Floto | US Cystic Fibrosis Foundation and European Cystic Fibrosis Society consensus recommendations for the management of non-tuberculous mycobacteria in individuals with cystic fibrosis: executive summary[END_REF][START_REF] Haworth | British Thoracic Society guidelines for the management of non-tuberculous mycobacterial pulmonary disease (NTM-PD)[END_REF].

Alternate diagnostic methods such as serological tests have been widely criticized, particularly in the context of tuberculosis, both in terms of highly variable clinical values across tests or studies [START_REF] Steingart | Commercial serological tests for the diagnosis of active pulmonary and extrapulmonary tuberculosis: an updated systematic review and meta-analysis[END_REF], and in terms of the financial costs of overtreating due to too many false positive results [START_REF] Dowdy | Serological testing versus other strategies for diagnosis of active tuberculosis in India: a cost-effectiveness analysis[END_REF]. Some meta-analyses carried out for NTM infections, for example on the diagnostic accuracy of the anti-M. avium IgA assay in the context of M. avium infections, also showed very large disparities in the clinical values [START_REF] Shibata | Diagnostic test accuracy of anti-glycopeptidolipid-core IgA antibodies for Mycobacterium avium complex pulmonary disease: systematic review and meta-analysis[END_REF].

Our main objective therefore was to study the presence or absence of antibodies against several antigens from M. abscessus in CF-patients with an NTM-positive culture. We did not consider defined clinical criteria or recommendations [START_REF] Griffith | An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases[END_REF][START_REF] Metersky | The association of long-term macrolide therapy and nontuberculous mycobacterial culture positivity in patients with bronchiectasis[END_REF], which are often poorly adapted to this patient population, or which required specific recommendations, mainly based on expert opinion [START_REF] Floto | US Cystic Fibrosis Foundation and European Cystic Fibrosis Society consensus recommendations for the management of non-tuberculous mycobacteria in individuals with cystic fibrosis: executive summary[END_REF][START_REF] Haworth | British Thoracic Society guidelines for the management of non-tuberculous mycobacterial pulmonary disease (NTM-PD)[END_REF], as we wanted to see if this antibody response could discriminate CFpatients with NTM-positive cultures, from CF-patients without an NTM-positive culture, and/or with sputum culture positive for fungi or other bacteria. Serological studies in patients with CF showed promising clinical values, although somewhat surprising regarding the antigen used, which is often a mycobacterial extract containing a cocktail mixture of mycobacterial antigens, and for which a pan-mycobacterial response should be found [START_REF] Ferroni | Measurement of immunoglobulin G against Mycobacterial antigen A60 in patients with cystic fibrosis and lung infection due to Mycobacterium abscessus[END_REF][START_REF] Qvist | Serodiagnosis of Mycobacterium abscessus complex infection in cystic fibrosis[END_REF][START_REF] Qvist | Evaluation of a bovine antibody test for diagnosing Mycobacterium avium complex in patients with cystic fibrosis[END_REF]. Nevertheless, it is important to be able to offer diagnostic alternatives to CF-patients infected by NTM, especially given the destructive potential observed in some NTM lung infections, such as those due to M. abscessus. Finally, we anticipate interest in such a serological test which would easily exclude CF patients with negative serology, as a rule out test, to focus solely on positive patients, particularly when considering the difficulties encountered due to the pitfalls of microbiological diagnosis by culture. Since CF patients are particularly well monitored clinically, with one NTM search per year in all sputum producing patients [START_REF] Floto | US Cystic Fibrosis Foundation and European Cystic Fibrosis Society consensus recommendations for the management of non-tuberculous mycobacteria in individuals with cystic fibrosis: executive summary[END_REF], this ELISA would also allow screening of this CF patient population to establish the risk of acquiring or developing a pulmonary NTM infection (risk stratification).

Among the antigens evaluated, only TLR2-activating enriched fraction (TLR2eF) and rPLC were performing well, with sensitivities of 61%, 64.4%, and specificities of 91.7% and 86.9% respectively for the detection of CF-patients with NTM-positive culture. These values were noticeably higher for the detection of M. abscessus-positive CF-patients, with sensitivities of 82%, 88%, and specificities of 89% and 84.5%, respectively.

Surprisingly, no reaction or only a low antibody response was seen for MAC-positive CFpatients, except for those CF-patients (4 out of 5) culture positive for M. intracellulare. This was not due to the characteristic of the antigens extracted from M. abscessus since the use of a M. avium subspecies-specific antigen (L5P) [START_REF] Biet | Lipopentapeptide induces a strong host humoral response and distinguishes Mycobacterium avium subsp. paratuberculosis from M. avium subsp. avium[END_REF] did not induce or demonstrate a strong antibody response in M. avium-positive CF-patients (5 out of 14) (supplementary Figure 4). The best grouping, according to our study, is to combine the results of TLR2eF and rPLC ELISA assays. Using this selection, we were able to increase the detection of CF-patients with NTM-positive culture, with sensitivity values of 66.1% and specificity values of 82.8%.

Similarly, the sensitivity value was even higher when looking at CF-patients with a positive M. abscessus culture (94%). This allows us to consider these two antigens as an excellent diagnostic complement for the characterization of CF-patients potentially infected with NTM, and especially with M. abscessus and M. intracellulare (supplementary Figure 3B and3D).

This will allow us to study them more precisely, on the clinical, radiological, and microbiological levels to understand the details of a positive response in TLR2eF and/or rPLC ELISA assays. Indeed, we can consider that the specificity values obtained are not optimal, compared to the 90% often expected, and that the false positive rate obtained is as a consequence of what we have just described above, linking the difficulties of culturing samples from CF-patients to the interest of an alternative approach to identify patients potentially infected by an NTM. Correlation with clinical and radiological evolution, as well as repeated microbiological examination of respiratory secretions, which is being carried out within the context of the prevalence study performed in France, will make it possible to solve the problem of specificity of these tests. Interestingly, and taking into account the "rule out test" strategy, the NPVs are very close to 99%, indicating that a negative serology is unlikely to be associated with a positive culture. This may allow us to set up a risk stratification strategy to differentiate the presence or absence of NTM infection by first performing the serological test. This strategy is currently being tested prospectively in our ongoing clinical study.

In conclusion, we present here two ELISA assays, which provide added value to the diagnostic arsenal we have at our disposal in the mycobacteriology laboratory when combining their respective results, since they improve the diagnosis of CF-patients with a NTM positive culture, and even more since they allow the serological confirmation of almost all CF-patients presenting a positive culture to M. abscessus. These two ELISA assays are currently being tested in a prospective study to determine the prevalence of NTM infections in CF patients (clinical trial n° ID RCB: 2017-A00025-48).

Figure 1 :

 1 Figure 1 :

Figure 1 :

 1 Figure 1: Flow chart. Names and compositions of the different studied groups.

Figure 2 :

 2 Figure 2 A B

Figure 3 :

 3 Figure 3 A B

table ,

 ,

	for Mabs
	culture positive CF-patients, and see the supplementary 2 by 2 Table 2A to 2D for MAC
	culture positive CF-patients.

Table 1 :

 1 Two by two table when combining TLR2eF and rPLC serology results versus NTM

	culture (A) or Mabs culture (B). Corresponding values of positive predictive value (PPV),
	negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio
	(NLR) and accuracy and their respective confidence interval at 95% (95% CI) of results
	presented in C and D.
	A				C
	TLR2eF or	NTM-positive	NTM-negative	Total	TLR2eF or		95% CI
	rPLC	culture	culture		rPLC
	Positive	39	25	64	PPV (%)	12.5	8.8 to 17.6
	serology				NPV (%)	98.5	97.8 to 98.9
	Negative	20	120	140	PLR	3.8	2.6 to 5.7
	serology				NLR	0.4	0.3 to 0.6
	Total	59	145	204	Accuracy (%) 82.2	76.2 to 87.1

  The response of CF-patients with a positive culture to M. intracellulare and M. chimaera also confirms that M. abscessus extracts can be used to recognize other NTM-culture positive CFpatients. Perhaps this represents a serological response identifying true NTM infections.Either way, the observed responses seem to correlate well with the two pathogens, M. abscessus and M. intracellulare, the NTMs currently most monitored. Non-response, or low anti-MAC response, may simply indicate colonization rather than infection, but again this needs to be further investigated in a prospective study.

As discussed in the context of tuberculosis, serological responses may vary from one individual to another and depending on the antigen used. The use of multiple antigens has shown reasonable success in some studies (39-43) but has never been looked at in the context of NTM infections in CF patients.

Acknowledgments

We sincerely thank Franck Biet (L5P) and Jérôme Nigou for their support and discussion concerning the antigenic extracts used in this study. We thank the members of the MucoMicrobes group (Association Vaincre la Mucoviscidose) for their unfailing support. We warmly thank Dr Ben Marshall (University Hospitals of Southampton, UK) for a thorough review and correction of the manuscript.

Financial support. This work was supported by grants from the Association Vaincre la Mucoviscidose (RF20110600446/1/3/130).

Potential conflicts of interest.

All authors declare no conflicts of interest regarding the study.