We would like to thank Vianney Forest (Inrap Méditerranée) for his help in defining the valve placement protocol, Sarah Ivorra and Angèle Jeanty (UMR 5554 ISEM) for their support with the analyses and Celia Lecomte for her participation in the inter-

Exploring AnciEnt shEllfish pAthwAys: A morphomEtric pErspEctivE on thE smooth scAllop Flexopecten glaber cypriEn murEAu, AllowEn Evin (umr 5554,isEm,montpElliEr,frAncE)

Background Methods

Material

Results

Implications

Smooth scallops are composed of two non symmetrical valves. We focussed on the right valve which is less swollen and has a large byssal indentation making it easier to cut out. A total of 729 right valves were studied. They come from 16 archaeological sites inhabited between the 1st and the 14th centuries.

Found in 76 stratigraphic contexts, the shellfish have been grouped into 27 samples. These conchylian samples were divided into 3 periods (Roman period, Late Antiquity, Middle Ages) and 5 regions, defined on the basis of the lagoon to which they are the closest (fig. 1).

The right valves were placed on plasticine according to a standardised protocol with strict positioning of the valve, inner side facing upward with anterior and posterior margins positioned parallel to the lens. Thus, inner faces were photographed using a Nikon d90 LSR camera paired with a 60 mm macro lens.

The background of each photograph was removed and the shell image converted to a black mask using Adobe Photoshop CS6 software. Because auricles are fragile and generally fragmented on archaeological specimens, they were manually erased from the images.

On the outline of each mask, 360 x,y coordinates were automatically extracted using R (v. Elliptic Fourier Transforms (EFT) were performed (Kuhl & Giardina, 1982). Outlines were decomposed into 7 harmonics and their coefficients were statistically analysed.

The smooth scallop Flexopecten glaber is an endemic species of the Mediterranean Sea. Its biology is not yet well understood, but it is a free-living shellfish that lives on sandy or grassy bottoms in waters no deeper than 80m.

From the Roman Period onwards, populations of Narbonnese Gaul made it one of the most consumed marine species along with the flat oyster Ostrea edulis (Bardot & Forest, 2013). These two seafoods have been carried to the hinterland and can archaeologically be found up to 300 km from the coast.

Archaeologists are still struggling to define the routes and methods by which Romans managed to transport shellfishes in an acceptable state of freshness. To trace the geographical origin of inland shellfish, we first explored the morphological variations between populations of the various lagoons where they are likely to have been caught.

To this end, we began by analysing the biogeography of the smooth scallop from the Antiquity to the Middle Ages in the Gulf of Lion. We used geometric morphometric analysis to quantify the shape of archaeological valves.

The shape variation was first assessed using a principal component analysis (PCA) computed on the EFT coefficients. Differences between samples were explored using MANOVAs and discriminant analyses.

Finally, the phenotypic dissimilarity between groups has been visualized by a neighbour-joining network computed on the mahalanobis distances.

The morphometric analysis of the shape of archaeological smooth scallop valves has successfully revealed significant morphological variation between different lagoons of southern France. However, the differences between chronological phases seem to be less significant.

These results suggest that the shape of smooth scallops is a good indicator of their geographical origin. By extending our corpus to include samples from consumer archaeological sites located in the hinterland, it may therefore be possible to estimate the location of the producers who supplied them.

We used geometric morphometrics through Elliptic Fourier Transforms (EFT) to quantify the morphology of the valves. The quantification of the outline of the vale was obtained through the following protocol (fig. 2).

The shape of the archaeological specimens overlap based on the two first axes of the PCA that represent 62.2 % and 24.5 % of the total variance (fig. 2.5). However, a two-way Procrustes MANOVA revealed significant shape differences between populations of different lagoons (F = 17.99, p < 2.2e-16) and dated from different periods (F = 4.23, p < 2.2e-16), but also that the geographic variation is not homogeneous through time (F = 3.39, p < 2.2e-16).

The native lagoon is correctly identified with a mean cross-validation percentages (CVP) of 76.8% (90% confidence interval, 74.1 -79.5 %). However, the discrimination between periods is less strong with CVP of 52.7% (49.8 -56.0 %).

The dissimilarity network confirms this trend, with a clear regional structuring on which the period seems to have had comparatively little impact (fig. 3). Thus, samples from the shores of a same lagoon are grouped in a same cluster, with the exception of samples from the Narbonne lagoon, which are in the middle of the network.

Provence

Regions :

  4.1.3) and the Momocs package (v. 1.4.0, (Bonhomme et al. 2014). The coordinates of 3 landmarks were manually acquired and used during the superimposition. They are localised at the apical tip of the internal ligament and at both ends of the hinge line. Position, orientation, and size were standardized by a Generalized Procrustes Analysis (GPA).

  Fig. 3 -Dissimilarity network between samples based on the shape of their valves. The color of the symbols differenciate their geographical origin, while the shape differenciate their dating.

Fig. 1 -

 1 Fig. 1 -Location of sites, classified by region, where the studied samples were discovered (map N. Langlois, Millaria Gallica, 17 th c.).

Acknowledgements

grateful to the excavation supervisors and the regional archaeology department of the Occitanie region, the museum curators and the regional archaeology service of the Occitanie region.

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 852573).


Caterina Cruciani

Léo Joubert

Nicolas Jullien

Laurent Mell

Sasha Piccione

Jeanne Vermeirsche

Affiliations

Surveying Wikipedians: a dataset of users and contributors' practices on Wikipedia in 8 languages Authors

Keywords: digital commons, contribution, information access, information trust, cultural activities

The dataset focuses on Wikipedia users and contains information about demographic and socioeconomic characteristics of the respondents and their activity on Wikipedia. The data was collected using a questionnaire available online between June and July 2023. The link to the questionnaire was distributed via a banner published in 8 languages on the Wikipedia page. Filling out the questionnaire was voluntary and not incentivised in any way. The survey includes 200 questions about: what people were doing on Wikipedia before clicking the link to the questionnaire; how they use Wikipedia as readers ("professional" and "personal" uses); their opinion on the quality, the thematic coverage, the importance of the encyclopaedia; the making of Wikipedia (how they think it is made, if they have ever contributed and how); their social, sport, artistic and cultural activities, both online and offline; their socio-economic characteristics including political beliefs, and trust propensities. More than 200 000 people opened the questionnaire, 102 862 started to answer, and constitute our dataset, and 10 704 finished it. Among other themes identified by future researchers, the dataset can be useful for advancing the research regarding the features of readers vs contributors of online commons, the relationship between trust, information, sources, and the use made of this information.

SPECIFICATIONS TABLE

Subject

Social science ; Political Science ; Sociology

Specific subject area

Use of a digital commons, Wikipedia: reading, contributing, access and use in the production of information, cultural activities Files joined to this paper data_raw.csv : the unfiltered database (102 862 respondents) data_filtered.csv : the filtered database (10 704 respondents) codebook.xlsx : codebook translated in 8 languages process.txt : file giving an explanation of database construction process

Data collection

Data were collected online via a questionnaire developed with the open-source limesurvey software. A banner was shown to those opening Wikipedia in the different participating languages, asking to answer a questionnaire. The banner would redirect to a LimeSurvey page managed by the research centre Marsouin.org and hosted in France on OVHCloud servers. The questionnaire (in English) is available here: https://questionnaires.marsouin.org/index.php/669835 The list of the languages in which the questionnaire is available can be found here: https://meta.wikimedia.org/wiki/Research:Surveying_readers_and_contributors _to_Wikipedia#Participating_languages The questionnaires were identical across the languages, with the same coding for the questions and the answers, and are all provided alongside the data. Different sources were used to build the questions, namely:

-the questions regarding the reader characteristics (reading habits) were taken from the questionnaire done in 2019 by the WMF; -the questions regarding reading behaviours and the assessment of the articles' readability benefited from the WMF's research "Research:Understanding perception of readability in Wikipedia" -the questions regarding the contribution dimensions were taken from the Marsouin's 2015 survey -the questions about the cultural dimensions were taken from the e-FRAN IDEE research and the ICT usages from the ICT usage in households and by individuals European survey -the socio-demographic data were aimed at capturing the reading/contributing gaps identified by the WMF and are compatible with it (and with the European's and Marsouin's surveys), for the trust/values questions are taken from the world value survey (wave 7);

Data source location

The survey was carried out online; thus, the data were collected from everywhere. We collected the IP address of the device used to answer by the respondents. This personal information is not shared for privacy reasons. The dataset provides the city and the country where the device used was located (city & country calculated from the IP address using the R library: rgeolocate) Data accessibility Data identification number: 10.34847/nkl.4ecf4u8m

Direct URL to data: https://nakala.fr/10.34847/nkl.4ecf4u8m

VALUE OF THE DATA

The dataset provides information about the social and cultural characteristics of Wikipedia users and how they impact Wikipedia as a source of information. "Wikipedia" is today's encyclopaedia of reference. Its users and uses are highly varied (information, reference, documentary research, for training or more personal purposes, etc.)1 . While there is a good understanding regarding its readers, how they use Wikipedia, and what acceptability as a source of information means, for them and for their "audience"2 past studies did not delve very deeply into the social and cultural characteristics of the different users of Wikipedia.

The dataset offers insights regarding the perceived reliability and quality of Wikipedia content by its different users, addressing whether different degrees of generalised trust and different habits in terms of information map onto different evaluations. At the same time, this data might contribute to the literature on trust in the online environment, providing a focus on the nature of the informationproduction process within Wikipedia and its impact on Wikipedia as an information source. Currently, the literature on trust in social media distinguishes clearly between trust in the person providing the news and trust in the source outlet (see for example Sterret et al., 2019). By distinguishing between different types of Wikipedia users this dataset allows us to explore whether users and creators subscribe to different trust processes.

This dataset may help better understand how people start contributing to Wikipedia, why they keep doing it, and how the project could help them do so while fostering participation. [START_REF] Mako | Wikipedia and the End of Open Collaboration[END_REF] creates a tension between the quality of the information published and the possibility that contributions pursue individual positions and might be used to push one's own agenda. To address these constraints the Wikipedia platform has made contributing increasingly difficult, raising the barrier to contribution for the newcomers (ibid), but the jury is still out regarding whether this approach produces higher quality content. This database allows us to dwell more deeply in the motivations and the everyday practice of Wikipedians to understand contributions to the platform as a component of a more general habit of processing information and producing content in order to address in a more holistic way the issue of contribution quality.

Underlying motivations and the evolution of the profile of contributors are one of the main concerns of scholarly research since the rise of Wikipedia [START_REF] Anthony | The quality of open source production: Zealots and good Samaritans in the case of Wikipedia[END_REF]. Given the complex and everevolving nature of Wikipedia, devising a theoretical and stable expectation about the evolution of contributors (not just in terms of numbers, but more importantly in terms of features and roles on the platform) is not easy. This database may help shed light on the relationship between some demographics and the likelihood of being and remaining a contributor. For instance, while we know that most wikipedians are male (Hill and Swah, 2013), we still need to establish how this specific feature interacts with roles taxonomy on the wiki or other biases in the coverage of wikipedia's corpus.

Thanks to its set structure, which reduces any further effect of the design, structure and content of the website interface, Wikipedia is a very interesting set up over which the development of online trust can be studied. By reducing the drivers on online trust to content features, trust and credibility of Wikipedia can be assessed in a more controlled environment. This database allows to address how different patterns of information collection and use impact upon the credibility of the platform and correlate with more general dimensions of generalised trust.

DATA DESCRIPTION

We provide two datasets, made available in the repository linked in the summary table:

• the raw dataset. It comprises 102 862 observations (amongst which only a half have really started to answer the questionnaire) obtained after removing duplicates identified by IP address matching (Sort rows so that the longest answers appear first. Filter duplicates by deleting individuals who answered all questions in the first part identically), • the filtered dataset. It comprises 10 704 observations and is obtained from the raw dataset, by keeping only individuals that answered at least two questions after the age question. This is what we call a complete response.

We also built a .csv file with the code for each variable and the corresponding question (in 8 languages). These datasets are fully anonymized, for instance we do not provide wikipedia's nicknames even when the respondent gave it.

  Wikipedia is a volunteer-based organisation where individual contributions shape the offered contents sometimes above and beyond what readers might want to read about. The emerging misalignment between what people want to read and what the contributors are willing to provide (Warncke-Wang et al., 2015), alongside the decrease in the number of regular contributors in the main Wikipedia languages (Hill

Table 1 . Summary of the diffusion process for each language.

 1

		Display dates	Banner exposure
	Languages surveyed (alphabetic order)	for the banner demanding people to questionnaire answer the	regime (probability that it appears on the page opened.
		(all 2023)

Page based, non (ip) session based Number of people who clicked on the banner (opened the questionnaire) Number of people who opened the questionnaire (went beyond the welcome page on the questionnaire site): our raw dataset Number of complete responses: our filtered dataset

		06/02 -> 06/12
	English	and 06/14 ->	5%	70829	35555	4989
		06/17
	French	06/12 -> 06/26	30%	22895	8129	815
	German	06/28 -> 07/12	20%	55948	21322	1993
	Italian	06/17 -> 06/31	50%	22077	9516	1092
	Portuguese	06/27 -> 07/11	30%	10162	3953	292
	Spanish	06/27 -> 07/11	30%	37302	18384	941
	Turkish	06/01 -> 06/15	50%	4657	1852	171
	Ukrainian	06/14 -> 06/28	50%	7002	4151	382
	Total	N/A	N/A	230872	102862	10675

Table 2 . Detailed view of the filtering process

 2 Nicolas Jullien: conceptualization, methodology, creation of the questionnaire, translation management, implementation on the platform (LimeSurvey), negotiation with the Wikimedia organisation to access the banners, translation in English Laurent Mell: Conceptualization, Methodology, Formal analysis, Data Curation Sasha Piccione: Formal analysis, Data Curation Jeanne Vermeirsche: Conceptualization, Methodology, translation in English

	Languages surveyed (alphabetic order)	Number of people who opened the questionnaire (went beyond the welcome page on the questionnaire site): our raw dataset	Number of people who answered at least up to the question about what they were doing on Wikipedia before clicking the link to the questionnaire	Number of people who answered at least up to the question about how they use Wikipedia as a reader ("professional" and "personal" uses)	Number of people who answered at least up to the question about their valuation of Wikipedia	Number of people who answered at least up to up to the questions about whether they had ever contributed to Wikipedia	Number of people who answered at least up to up to the questions about their level of contribution	Number of people who answered at least up to their relationship to formation trust information/in	Number of characteristics people who answered at least up to their socio-economic	Number of complete responses: our filtered dataset
	English	35555	30664	16909	12491	9117	8306	6050	1880	4989
	French	8129	5684	3366	1730	1272	1180	917	817	815
	German	21322	16647	10233	5121	3817	3197	2171	1969	1993
	Italian	9516	7763	4854	2338	1724	1552	1193	1125	1092
	Portuguese	3953	3149	1743	791	562	448	313	292	292
	Spanish	18384	15822	8200	3261	2205	1896	1119	967	941
	Turkish	1852	1486	677	449	275	234	173	166	171
	Ukrainian	4151	3947	2166	952	664	594	458	393	382
	Total	102862	85162	48148	27133	19636	17407	12394	7609	10675

On the history of Wikipedia, its impact and how it works, we recommend reading the book published by Reagle etKoerner (2020)

see the survey of Wikipedia's readers in 14 languages byLemmerich et al., 2019

ACKNOWLEDGEMENTS

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

It has received the help of several persons for the translation in this endeavour: User:Danielly Campos Dias for the Portuguese, User:Antanana for the Ukrainien, Müge Ozman for the Turkish, Anne Bartel-Radic re-read by utilisateur:Charlik for the German, François Élie who reread the French, Tania Jimenez for the Spanish (re-read by User:oscar-costero).

We benefited also from numerous feedback from the local communities before the official launch of the 2 week survey periods when we announced the questionnaire in the dedicated spaces of the village pumps.

We also received help from Anaïs Mazurier for the scrub data.

EXPERIMENTAL DESIGN, MATERIALS AND METHODS

The dataset is the result of a survey carried out through LimeSurvey. The questionnaire was completed on a voluntary basis after clicking on a Wikipedia "banner" that would appear on the top of the Wikipedia page the users were browsing. The language of the questionnaire depended on the language in which the respondent opened Wikipedia (a banner in French targeting the questionnaire in French was published on the fr.wikipedia.org, in German for de.wikipedia.org, and so on) (See Table 1 and 2 for the complete list of languages). The questionnaire was available for a total of two weeks in each language. The overall data-collection process took place between the beginning of June 2023 and the middle of July 2023. Please refer to table 1 and 2 below for more information about the timeframe and events associated with the data collection and data curation.

The questionnaire was originally developed in French. It had been translated using DeepL and then reviewed by native speaking people (researchers or members of the Wikipedia community). The questionnaire was presented in the different languages' discussion spaces (in en.wikipedia: the Village Pump, in fr.wikipedia le bistro, and so on, but for one, as the community of de.wikipedia does not favour this type of annonce). The translations include local adaptation to make the questions more relevant to the different languages, such as the examples used for the scholar system to the local context, the questions regarding the language used (such as "is French your mother tongue" becoming in the Portuguese questionnaire "is Portuguese your mother tongue", etc.)

For each language but for the English we asked if the people had already read / contributed to the English Wikipedia, in addition to the Wikipedia they were using when they clicked on the banner.

LIMITATIONS

Wikipedia is an ever growing community that spans across the globe; thus, it is difficult to identify the precise composition of this community. Given that the participation in this study was completely voluntary and not incentivised.

Moreover, self-selection bias might have played a role in participation. We cannot guarantee or even make the assumption that the collected sample is representative of the actual communities surveyed because we cannot grant that respondents and non-respondents are similar even given some set of characteristics.

Further, self selection and cultural features might have produced unequal response rates across the different languages. Regarding languages, their relative weight in the final sample is different and favour the English Wikipedia, but once again we cannot be certain that this over-representativeness is consistent with the actual population rates.

ETHICS STATEMENT

Informed consent was obtained from participants and participant data has been fully anonymized

CREDIT AUTHOR STATEMENT

Caterina Cruciani: question about trust in the media, translation in Italian Léo Joubert: Conceptualization, Methodology, proofreading of Spanish translation

DECLARATION OF COMPETING INTERESTS

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Abdelhadi Essamlali
email: abdelhadi.essamlali@utc.fr

Vincent Millot-Maysounabe

Marion Chartier

Grégoire Salin

Aymeric Becq

Lionel Arrivé

Marine Duboc Camus

Jérôme Szewczyk

Isabelle Claude

Bile duct segmentation methods under 3D slicer applied to ERCP: advantages and disadvantages

Keywords: Segmentation, biliary tract, MRI images, ERCP, U-Net

This article presents an evaluation of biliary tract segmentation methods used for 3D reconstruction may be very usefull in various kind of critical interventions such as endoscopic retrograde cholangiopancreatography (ERCP) using the 3D Slicer software. This article provides an assessment of biliary tract segmentation techniques employed for 3D reconstruction, which can prove highly valuable in diverse critical procedures like endoscopic retrograde cholangiopancreatography (ERCP) through the utilization of 3D Slicer software. Three different methods, namely thresholding, flood filling, and region growing, were assessed in terms of advantages and disadvantages. The study involved 10 patient cases and employed quantitative indices and qualitative evaluation to assess the segmentations obtained by the different segmentation methods against ground truth. The results indicate that the thresholding method is almost manual and time-consuming, while the flood filling method is semi-automatic and also time-consuming. Although both methods improve segmentation quality, they are not reproducible. Therefore, an automatic method based on region growing was developed to reduce segmentation time, albeit at the expense of quality. These findings highlight the pros and cons of different conventional segmentation methods and underscore the need to explore alternative approaches such as deep learning to optimize biliary tract segmentation in the context of ERCP.

Introduction

Endoscopic retrograde cholangiopancreatography (ERCP) is a procedure intended for the treatment of biliary stones (which affects 2-4% of the general population) and biliary or pancreatic neoplasia. In France, approximately 78,000 ERCP procedures are performed each year for this purpose [START_REF]Hépato-Gastroentérologie[END_REF]. This invasive procedure is technically complex, as it involves two distinct steps which can pose a technical challenge. The first step consists in accessing the major papilla, located in the duodenum, using a side-viewing endoscope called a duodenoscope. The second step, called biliary cannulation or catheterization, involves penetrating the bile ducts via the major papilla, using a guidewire-loaded catheter. ECRP fails in 2% of cases and adverse events are reported in 4 to 9% of cases [START_REF] Dumonceau | ERCP-related adverse events: European Society of Gastrointestinal Endoscopy (ESGE) Guideline[END_REF]. Most challenge cases are represented by biliary malignancies, with infiltrating lesions of the biliary tree, with the risk of cholangitis and delay chemotherapy or surgery in case of wrong biliary drainage during ERCP.

Understanding the biliary anatomy is critical, but poses a significant challenge due to the complexity of the biliary tree and the interindividual anatomic variations. The success of ERCP can be hindered by a limited comprehension of biliary anatomy. This claim is supported by recent findings in an article by Becq et al [START_REF] Becq | ERCP 2.0: Biliary 3D-Reconstruction in Patients with Malignant Hilar Stricture[END_REF] on this topic. While preoperative imaging techniques such as abdominal CT and cholangio-MRI images, as well as intraoperative guidance with 2D fluoroscopy provide useful information (see "Figure 1"), they do not provide a comprehensive overview of the patient biliary anatomy. Hence, segmentation and 3D reconstruction of the biliary tract based on cholangio-MRI images is an interesting prospect to overcome this limitation [START_REF] Becq | ERCP 2.0: Biliary 3D-Reconstruction in Patients with Malignant Hilar Stricture[END_REF]. Reliable and robust automated segmentation could improve the accuracy and efficiency of diagnosis [START_REF] Marc H Goldfinger | Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges[END_REF], help better plan the procedure and allow a significant time and X-ray exposure saving for patients and physicians.

However, cholangio-MRI automated segmentation faces many challenges: small target volume, uneven image brightness, low spatial resolution (especially in the inter-slice direction [START_REF] Robinson | Segmentation of the Biliary Tree in MRCP Data[END_REF]), variable image quality, poor contrast in the intrahepatic ducts (IHDs) [START_REF] Ralli | Segmentation of the Biliary Tree from MRCP Images via the Monogenic Signal[END_REF], presence of bifurcations, tortuous bile ducts, and bright gastrointestinal structures. The most important challenge remains the absence of a signal in the case of stenosis because the cholangio-MRI is a sequence that detects bile and not the walls of the bile ducts. In those conditions, manual segmentation, although feasible and robust [START_REF] Becq | ERCP 2.0: Biliary 3D-Reconstruction in Patients with Malignant Hilar Stricture[END_REF], is incredibly timeconsuming.

Very few studies on bile duct segmentation have been published. Ivashchenko et al. [START_REF] Oleksandra V Ivashchenko | A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images[END_REF] employed multiscale vesselness filters from dynamic MRI images with liver-specific contrast agents to perform bile duct segmentations. The study tested the algorithm using 15 patient data sets with different types of liver malignancies. The results were evaluated by calculating the average Hausdorff distance between the central lines of the bile ducts. However, calculating the center lines of the generated segmentations is insufficient to understand the patient's bile duct anatomy, including strictures and dilatations. Some other studies employed segmentation tasks to classify specific types of diseases. For instance, Al-Oudat et al. ( 2021) [START_REF] Atallah Al-Oudat | An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision[END_REF] utilized the active contour method to segment the biliary tree and extract features. After feature extraction, they employed different classifiers to determine the health status of patients, whether they were normal or dilated. Although this study establishes a scientific correlation between the extracted features and the state of the biliary tree, the authors did not specify how they validated the results of their automatic segmentations.

Ralli et al. [START_REF] Ralli | Segmentation of the Biliary Tree from MRCP Images via the Monogenic Signal[END_REF] conducted a study on the improvement of the quality of cholangio-MRI images and found that the regularized phase symmetry filter can enhance the bile ducts, suppress the background, and improve the image without distorting or suppressing the intrahepatic bile ducts, suggesting that it may be a better option for preprocessing biliary MRI images and achieving more accurate bile duct segmentations.

The aim of our study was to evaluate the accuracy of three classical segmentation methods compared to manual segmentation (ground truth) in terms of their respective advantages and disadvantages. These methods were applied within the specific context of cholangio-MRI image segmentation. The evaluation was conducted using quantitative and qualitative parameters, aiming to highlight the differences between these practices.

Materials and Methods

Methodology

The methodology used for bile duct segmentation based on cholangio-MRI images, as well as their evaluation by comparing the automated segmentations with the manual segmentations (ground truth) can be seen in "Figure 2".

The Cholangio-MRI sequence images undergo preprocessing, including contrast enhancement to homogenize the images and enhance the signal-to-noise ratio. Subsequently, segmentation is performed using three techniques: thresholding via 3D Slicer, a quasi-manual method involving a defined intensity threshold; flood filling via 3D Slicer, a semi-automated approach; and region growing, another classical semi-automated method. These methods enable the delineation of regions of interest. Post-processing involves removing undesired anatomical elements from the segmented regions to enhance the outcomes. In parallel, manual segmentation is carried out directly on the Cholangio-MRI images using 3D Slicer to establish a ground truth reference. Based on this ground truth, the results of the three aforementioned segmentation methods are quantitatively assessed using measures and metrics, as well as qualitatively evaluated by experts who assess the accuracy and relevance of the segmentations.

Manual and calculated segmentations

In order to quantify the performance of the segmentation methods, a robust ground truth via manual segmentation is essential. Manual segmentation of all cases was performed, slice by slice, by gastroenterologists using the 3D Slicer software (Version 4.13). Manual segmentation is very time-consuming, taking between 20 and 30 hours per model. These segmentations were subsequently subjected to review and validation by an expert radiologist, who verified the absence of outliers, false communicating IHDs * , and false non-communicating IHDs * [START_REF] Becq | ERCP 2.0: Biliary 3D-Reconstruction in Patients with Malignant Hilar Stricture[END_REF]. For each sequence, three calculated segmentations were generated. First, two segmentations based on semi-automatic tools were performed with the 3D Slicer software: a segmentation based on the thresholding of pixels according to their intensity, and a segmentation based on flood filling, a classical algorithm that groups together a related set of pixels of the same intensity delimited by contours. Then, a third segmentation based on an automatic method of the region growing type developed internally at BMBI laboratory, was performed. This type of segmentation starts with a seed point or region and iteratively adds neighboring pixels or regions that have similar properties.

We chose to segment a majority of challenging ERCP cases with malignant hilar strictures, for which gastroenterologists may not achieve optimal results, especially in the case of hilar cholangiocarcinoma (CCK) [START_REF] Becq | ERCP 2.0: Biliary 3D-Reconstruction in Patients with Malignant Hilar Stricture[END_REF]. For these patients, the manual segmentation step took approximately 200 to 300 hours of work.

* IHDs (intrahepatic ducts)

Dataset

Cholangio-MRI images of patients treated in the digestive endoscopy department of Saint-Antoine Hospital were collected in a DICOM format and de-identified. This study responds to MR004 guidelines in France on retrospective studies of anonymized data and did not require ethics committee approval.

Cholangio-MRI images are based on the bile hypersignal in T2 weighted MRI sequences, characterized by a short TE (700 to 884 ms) and a long TR (3500 to 4000 ms), and cancelling out of the surrounding tissue signal in order to retain the bile signal only. These sequences therefore allow (without using contrast medium) visualization of the bile ducts. The image sequences are acquired in the coronal plane. Ten patients were included for analysis, two with normal bile ducts and eight with malignant stenosis at the level of the hilum.

Imaging was performed on a GE Medical Systems scanner, Optima MR 450 w, with a magnetic field of 1.5 T. The accuracy of segmentation strongly depends on the spatial resolution of the images. In our case, the size of voxel was between (1.094 x 1.094 x 1.5

) and (0.664 x 0.664 x 2 ).

Data preprocessing

Cholangio-MRI images have variable characteristics (contrast, noise, average intensity value). Pre-processing thus plays a fundamental role in the performance of image segmentation algorithms in terms of capacity and visual quality [START_REF] Nuñez-Ramirez | Improving preprocessing in reversible data hiding based on contrast enhancement[END_REF].

In order to facilitate the segmentation, the contrast between the structure of interest (i.e., bile duct) and the surrounding structures (background, surrounding organs) was maximized in two different ways [START_REF] Perumal | Preprocessing by Contrast Enhancement Techniques for Medical Images[END_REF]: manually on the 3D Slicer software for the thresholding and flood filling methods, and automatically for the region growing method using dynamic cropping (this technique was compared to Wiener denoising filter and showed better results).

Segmentation methods

Thresholding

The thresholding method is a well-known technique in the field of medical image segmentation [START_REF] Senthilkumaran | Image Segmentation By Using Thresholding Techniques For Medical Images[END_REF]. This technique relies on the definition of a threshold value T such that the threshold image g is obtained by: (

Where:

: the coordinates of the pixel.

: the gray level of the pixel in the image f.

The global thresholding technique is mainly used when the intensity distribution between the foreground and background structures is very distinct. In that case, a single threshold value can simply be used to differentiate the two structures. This is not the case in cholangio-MRI images however [START_REF] Senthilkumaran | Image Segmentation By Using Thresholding Techniques For Medical Images[END_REF]. Hence, we decided to use two different thresholds and , allowing the definition of a range of intensity to be segmented.

In this case the threshold image is defined by:

(

In order to achieve a fully segmented biliary tree while minimizing unwanted elements, the and windowing values were manually and carefully selected in 3D Slicer, varying from one sequence to another and even from one slice to another.

Flood filling

Flood filling algorithm is a region-approach segmentation technique which leads to fill a region of an image with a given grayscale value [START_REF] Rafael | Digital Image Processing[END_REF]. The principle of the flood filling algorithm for grayscale image segmentation follows the following steps:

 Select a starting point on the image.

 Specify a grayscale value for the desired fill.

 Set a threshold tolerance to define the grayscale range for filling. This range determines neighboring values within which filling will occur.

 Add the starting point to a stack.

 While the stack is not empty, repeat the following steps:

-Retrieve the first element of the stack and pop it.

-If the current point is within the image and its grayscale intensity is within the tolerance range, change its grayscale intensity to the target fill color. -Add to the stack the neighboring pixels that have not yet been filled and whose grayscale color is within the tolerance range.

Region growing

The region growing method is a region-approach segmentation technique which can be broken down into two steps:  The selection in the image of one or more priming areas, often called "seeds" and serving as starting points for the next step. The selection of these areas can be automatic, using maximum or minimum light intensity for example, but it is also possible to ask the user to directly select areas of the image.

 The next step is a step of growing of the starting regions: the idea is to gradually grow the seed regions by aggregating the pixels that meet a criterion, called the homogeneity criterion. This criterion ensures that the same region grows by aggregating pixels with similar characteristics. It should be noted that only one priming zone is required to segment all the images within a cholangio-MRI by relying on propagation.

After comparing several aggregation criteria, (Otsu threshold, detector of Canny, and fixed threshold, Sauvola's threshold), we chose the Sauvola's threshold because it lead to better segmentation [START_REF] Senthilkumaran | Image Segmentation By Using Thresholding Techniques For Medical Images[END_REF]. The Sauvola threshold, is widely used as a local thresholding method [START_REF] Sauvola | Adaptive document image binarization[END_REF]. The Sauvola approach calculates the threshold for each pixel based on the mean and standard deviation of pixel intensities within a local neighborhood. The equation for the Sauvola threshold is defined as follows:

(3)

In this equation, represents the computed threshold for the pixel at position in the image. denotes the set of intensity coefficients of pixels within a local 3x3 pixel area centered around pixel . The parameters and play key roles in this equation:

is a sensitivity parameter that controls the influence of local variation on thresholding. A higher value of makes the thresholding more stringent, while a lower value makes it more tolerant.

is a scale parameter that adjusts the standard deviation. Increasing it gives more consideration to the local standard deviation in the threshold calculation.

By adjusting these parameters, including using =0.3 and =100 for cholangio-IRM images, we can fine-tune the Sauvola threshold to better align with the specific characteristics of the image and local intensity variations.

Evaluation criteria 2.5.1. Quantitative evaluation criteria

In this paper we used three evaluation indicators; the Dice similarity coefficient (DSC), the Hausdorff distance (HD) and the Relative Volume Difference (RVD) to measure the accuracy and quality of the three segmentations generated.

Given a set of pixels belonging to a ground truth, and , a set of pixels belonging to a segmented object, the Dice index is defined as follows [START_REF] Sirinukunwattana | Gland segmentation in colon histology images: The glas challenge contest[END_REF]: [START_REF] Marc H Goldfinger | Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges[END_REF] is an indicator of the largest segmentation error. As illustrated in "Figure 3", for two point sets and , the one-sided from to is defined as [START_REF] Karimi | Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks[END_REF]: [START_REF] Robinson | Segmentation of the Biliary Tree in MRCP Data[END_REF] and similarly: (6) Figure 3. A schematic showing the hausdorff distance between points sets X and Y [START_REF] Karimi | Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks[END_REF].

Finally, the Relative Volume Difference is calculated by taking the absolute difference between the volume of the automatically generated segmentation and the manually generated segmentation , divided by the volume of the manual segmentation [START_REF] Nai | Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset[END_REF].

is defined as:

(7)

Qualitative evaluation criteria

In order to assess the quality of the segmentations generated by the three different methods, a qualitative evaluation was performed by three expert gastroenterologists. The evaluators first assessed the similarity in caliber and completeness of the common bile duct and intrahepatic bile ducts. Then, they visually counted the number of outliers, false communicating IHDs, and false non-communicating IHDs in the segmentations, as shown in "Figure 4, 5 and 6" respectively. The evaluators were blinded to the segmentation method used and asked to reach a consensus agreement for each case and each segmentation. The manual segmentation was defined as the gold standard for these evaluations.

In the context of bile duct segmentation, outliers typically refer to segmentation errors in which parts of the bile duct structure are either incorrectly segmented or are missed altogether "Figure 4''. This can occur due to a variety of factors, such as variations in image quality, imaging artefacts, or limitations in the segmentation algorithm.

False communicating IHDs in bile duct segmentation refer to segmentation errors in which regions of the image that are not part of the bile duct structure are included in the segmentation and connected to the bile ducts "Figure 5''. These errors can lead to the incorrect connection of segments in the biliary tree that are not actually connected, resulting in a potentially misleading representation of the biliary anatomy. False connections can occur due to various factors, such as differences in image quality, imaging artefacts, or limitations in the segmentation algorithm. In particular, anatomical structures in the vicinity of the bile ducts that have similar signal intensities can contribute to the occurrence of false connections.

False non-communicating IHDs occur when two adjacent segments of the bile duct are mistakenly identified as distinct entities, resulting in the erroneous division of a single segment into two separate parts "Figure 6''.

These types of segmentation error can cause confusion and misinterpretation of the anatomy of the bile duct, potentially leading to incorrect medical decisions. As such, avoiding outliers, false communicating IHDs and false non-communicating IHDs is an important goal in the accurate segmentation of the bile duct.

Statistical analysis

A statistical analysis was conducted to determine if there were any significant differences between the techniques. This analysis was performed using the GraphPad Prism 8.4.3 software (GraphPad, San Diego, CA, USA). A one-way ANOVA test was employed. The results were considered statistically significant if the p-value was less than 0.05, which is a common threshold for determining significance. In the figures, any data with a p-value less than 0.05 were highlighted with an asterisk to indicate their significance.

Experimental Results

Automated segmentation using the three methods (thresholding, flood filling, and region growing) was feasible in all cases "Figure 7". The mean DSC values were 0.819 ± 0.057, 0.772 ± 0.059, and 0.657 ± 0.110 for thresholding, flood filling, and region growing, respectively. The HD were 0.816mm ± 0.353, 1.291mm ± 0.656, and 1.963mm ± 1.091 for thresholding, flood filling, and region growing, respectively. The RDV values were 0.188 ± 0.118, 0.235 ± 0.172, and 0.539 ± 0.349 for thresholding, flood filling, and region growing, respectively.

The number of outliers for the three methods were 6.2 ± 3.86, 8.2 ± 3.15, and 7.9 ± 3.75. The number of communicating IHDs switched to non-communicating IDHs were 2.0 ± 2.00, 2.1 ± 1.57, and 4.8 ± 6.24 for thresholding, flood filling, and region growing, respectively. Finally, the number of non-communicating IDHs switched to communicating IDHs were 0.2 ± 0.40, 0.0 ± 0.00, and 0.1 ± 0.30 for thresholding, flood filling, and region growing, respectively. These results indicate that thresholding had the highest mean DSC value, the lowest HD, and the lowest RDV, while all three methods had a large number of communicating IHDs switched to non-communicating and a large number of outliers. These results can be seen in table 1.

The Thresholding method exhibited a significantly better performances (quantitative and qualitative criteria) (p=0.05) in bile duct segmentation compared to the Region growing method "Figure 8''. This is due to the possibility of manually choosing the threshold for each area in the biliary tree (CBD, HH, and IHD) (which is not reproducible) as opposed to the other two methods. Except for the DSC, there is no significant difference between the two region-based approach methods (Flood filling and Region growing). However, none of the three methods are robust for bile duct segmentation, as evidenced by a Dice score < 0.82 and a Hausdorff distance > 0.816.

Discussion and Future work

We will initiate the discussion by contextualizing our findings within the existing body of knowledge. Understanding the intricate anatomy of the bile ducts is crucial for the success of endoscopic retrograde cholangiopancreatography (ERCP). However, its delicate nature, coupled with interindividual anatomical variations, poses significant challenges. Although preoperative imaging techniques such as abdominal CT and MRCP, along with intraoperative 2D fluoroscopy, provide valuable insights, they lack a comprehensive overview. In this context, the segmentation and 3D reconstruction of the bile ducts from cholangio-MRI images offer a promising solution to address this limitation [START_REF] Becq | ERCP 2.0: Biliary 3D-Reconstruction in Patients with Malignant Hilar Stricture[END_REF]. Yet, manual segmentation, despite its robustness, is time-consuming, underscoring the need for automated solutions to enhance diagnostic accuracy and procedural efficiency [START_REF] Marc H Goldfinger | Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges[END_REF], while also reducing X-ray exposure for both patients and physicians.

Despite the potential advantages, automated cholangio-MRI segmentation faces various challenges, including image brightness disparities, low spatial resolution, variable image quality, and the inability to detect bile duct walls in cases of stenosis. Previous research has explored techniques such as multiscale vesselness filters [START_REF] Oleksandra V Ivashchenko | A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images[END_REF], phase symmetry filters [START_REF] Ralli | Segmentation of the Biliary Tree from MRCP Images via the Monogenic Signal[END_REF], and active contour methods [START_REF] Atallah Al-Oudat | An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision[END_REF], but none have provided a fully robust solution.

Our study rigorously evaluated classical segmentation methods, unveiling their shortcomings in cholangio-MRI image segmentation. DSC values, HD measurements, and RDV values illuminated the limitations of thresholding, flood filling, and region growing methods. Notably, none of these methods achieved the desired robustness, indicating a critical need for innovative alternatives.

However, upon a more comprehensive analysis of the results, we were able to identify specific areas where these methods exhibited tendencies toward over-segmentation or under-segmentation of the regions of interest. For instance, in certain regions with indistinct boundaries, the region growing method was prone to incorporating surrounding pixels that were not inherently part of the region in question, thereby leading to a slight over-segmentation (identified by a white circle in "Figure 7"). Conversely, in regions characterized by low contrasts, the Flood filling method occasionally omitted portions of the region, resulting in under-segmentation (identified by an orange circle in "Figure 7"). These limitations (under/over segmentations) result in false communicating IHDs and false non-communicating IHDs in the 3D reconstruction. A qualitative evaluation of the segmentations by three expert endoscopists was conducted. The results are presented in "Table 1". This qualitative evaluation of classical bile duct segmentation methods revealed several drawbacks. Firstly, these methods tend to create false connections between intrahepatic bile ducts, potentially leading to misinterpretation and incorrect treatment decisions during CPRE, with exposure to infectious risks, with a particularly high risk of prosthetic misplacement. Secondly, these methods fail to provide a complete segmentation of the biliary tree, resulting in gaps that hinder practical use by endoscopists. Other less significant limitations of these classical segmentation methods include over-segmentation and the presence of outliers.

. To address these gaps, we are currently leveraging and developing a U-Net-based model for precise cholangio-MRI segmentation. Our approach yielded promising results, confirming its effectiveness in terms of segmentation accuracy and quality. While these results are encouraging, further work is essential to consolidate and generalize our conclusions across a broader patient cohort. We intend to extend the application of our model to various hepatobiliary conditions, evaluating its adaptability and robustness across diverse clinical scenarios.

Future improvements to our model encompass exploring U-Net architecture variations and integrating advanced deep learning techniques such as pre-trained networks and attention mechanisms. These endeavors aim to enhance segmentation performance and result quality. Concurrently, our data augmentation pipeline will be refined to improve model generalization, accommodating the inherent clinical variability in cholangio-MRI images.

Conclusion

In this study, three classical techniques used for bile duct segmentation based on cholangio-MRI images were evaluated. Our results suggest that these methods are not suitable for clinical practice, as they fail to provide perfectly accurate results. Given that manual segmentation is time consuming, development of a rapid, automated, reproducible and accurate alternative method is warranted. According to our preliminary findings, deep learning techniques exploiting a large number of cholangio-MRI images may provide a viable solution. This approach may overcome the limitations of conventional methods. The development of a new method based on deep learning techniques is currently being conducted by our team.

Figure 1 .

 1 Figure 1. ERCP image showing the endoscope, the biliary tree and the main pancreatic duct.

Figure 2 .

 2 Figure 2. Automated Segmentation of Bile Ducts from Cholangio-MRI Images and Comparative Evaluation

Figure 4 .

 4 Figure 4. Example of outlier, Ground truth (A.1), Calculated segmentation (A.2).

Figure 5 .

 5 Figure 5. Example of false communicating IHDs, Ground truth (B.1), Calculated segmentation (B.2).

Figure 6 .

 6 Figure 6. Example of False non-communicating IHDs, Ground truth (C.1), Calculated segmentation (C.2).

Figure 7 .

 7 Figure 7. Comparison of bile duct segmentation for the three methods with ground truth (bleu box is labeled CBD, yellow box is labeled HH, red box is labeled IHD, white circle: example of over-segmentation, orange circle: example of under-segmentation), CBD: Common bile duct, HH: Hepatic hilum, IHDs: Intrahepatic ducts.

Figure 8 .

 8 Figure 8. Graphs of DSC, HD, RDV, Outliers, False communicating IHDs and False non-communicating IHDs for the three segmentation methods.

Table 1 .

 1 Experimental results of bile duct segmentation Hausdorff distance, RDV: Relative Volume Difference, IHDs: Intrahepatic ducts

	Segmentation		Quantitative evaluation criteria			Qualitative evaluation criteria
	method	DSC	HD (mm)	RDV	Outliers	false communicating IHDs	false non-communicating IHDs
	Thresholding	0.819 ±0.057	0.816 ±0.353	0.188 ±0.118	6.2 ±3.86	2.0 ±2.00	0.2 ±0.40
	Flood filling	0.772 ±0.059	1.291 ±0.656	0.235 ±0.172	8.2 ±3.15	2.1 ±1.57	0.0 ±0.00
	Region growing	0.657 ±0.110	1.963 ±1.091	0.539 ±0.349	7.9 ±3.75	4.8 ±6.24	0.1 ±0.30

DSC: Dice, HD:

Acknowledgements

This work is a component of a continuous research initiative that involves collaboration between the Saint-Antoine and Mondor Hospital of the AP-HP, the Institute of Intelligent Systems and Robotics (ISIR), and the Laboratory of Biomechanics and Bioengineering (BMBI). The principal aim of this initiative is to enhance and ensure the overall safety of ERCP. We would like to extend our sincere gratitude to the Labex CAMI (ANR-11-LABX-0004), which provided essential support for this work through the Investissements d'Avenir program of the French Government.


Dr Mickael Ehrminger

Luz Adriana Bustamante Perez

Pr Lucia Romo

Effet

Luz Adriana Bustamante

Effet d'un programme de sevrage sur les sentiments de volonté, de préparation au changement et d'auto-efficacité

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Kwit a développé un programme de préparation1 au sevrage tabagique en neuf étapes. Dans ce programme, les utilisateurs sont invités à évaluer, au cours de la première et de la dernière étape, leurs sentiments de volonté, de Résultats Entre le 14 septembre et le 13 octobre 2021, 6808 utilisateurs ont commencé le programme, et 383 l' ont terminé (30 avec le parcours complet et 353 avec le parcours raccourci). Il n'y avait pas de différence significative entre les deux sous-groupes en termes de genre d' âge. Entre le premier jour et le dernier jour programme, les trois indicateurs motivationnels ont augmenté significativement à l' échelle de l' échantillon total, et dans les deux parcours. La taille d'effet est environ deux fois plus importante avec le parcours complet par rapport au parcours raccourci. Aussi, pour le parcours raccourci, la d'effet est plus forte chez ceux ayant des niveaux initiaux (volonté, n = 24 ; auto-efficacité, n = 111 ; préparation, n = 44), avec des d de Cohen entre 1.2 et 1.3. Le faible nombre d'utilisateurs des niveaux initiaux inférieurs à 5 dans le parcours complet ne permet pas cette analyse dans ce sous-groupe. Adopter une attitude d'observateur

Observer les habitudes de consommation actuelles par la saisie des envies au cours de la journée type.

Identifier les valeurs personnelles et les motivations pour arrêter de fumer

Le point de

  dans le d'une thèse CIFRE entre Paris Nanterre et Kwit SAS, par Luz Adriana BUSTAMANTE PEREZ, psychologue en TCC et supervisée par Pr. Lucia ROMO Comprendre l'interaction En savoir plus sur la dépendance physique Comprendre son parcours Observer les habitudes de consommation passées Agir en connaissance de cause Classer les pulsions en ancrés (routiniers) et réflexes (contextuels).Explorer mes croyancesEn savoir plus sur la psychologique Créer la vie que je En savoir plus sur la dépendance comportementale Explorer ses ressources Essayer de fixer des objectifs : 1. Fixation d' un objectif (comportement) 2. Planification de l' action : contexte, fréquence, durée ou intensité du comportement (dans quel contexte je vais fumer)

  préparation au changement et d'auto-efficacité, trois variables pour réussir son sevrage. Les étapes sont composées d' activités fondées les méthodes cognitives et comportementales, recommandées par la Haute Autorité de Santé2. Le programme en deux versions : 4 ou 9 a. Département Recherche & Développement, Kwit SAS : mickael@kwit.app b. Doctorante CIFRE en psychologie, avec le soutien financier l' ANRT et la collaboration du Laboratoire Clinique Psychanalyse et Développement (CLPSYD -EA4430) et entreprise KWIT SAS. c. Professeure de psychologie clinique à l'Université Paris Nanterre et psychologue l'Hôpital Raymond Poincaré et l'Hôpital de Garches (APHP) : romodesprez@gmail.com *Correspondance à adresser à : sciences@kwit.app

	Méthodes
	L' évaluation subjective de l' état initial et des sentiments de volonté, de préparation et d' auto-efficacité a été réalisée avec une échelle visuelle analogique de 1 à 10 (Readiness Ruler, voir ci-dessous).
		évaluation du niveau motivationnel
	Retour	1/ 3	Retour	2/ 3	Retour	3/3
	A quel point changer est-il une priorité pour vous en ce moment ?	A quel point avez-vous confiance en vos capacités de changement ?	A quel point vous sentez-vous prêt à passer à l'action ?
	Votre capacité d' atteindre votre objectif dépend de la priorité qu'il a dans votre vie.				Lorsque vous entreprenez un parcours de changement, vous passez étapes.
		Continuer		Continuer		Confirmer

Votre confiance en vous joue un grand rôle dans votre capacité à atteindre vos objectifs. La motivation est impactée par votre votre niveau de confiance à changer et vos priorités du moment . Faites le point pour savoir d'où vous partez : cela aidera à adapter votre itinéraire et vos ressources ! Introduction changement à deux moments : étape 1 étape 9 Nous avons étudié l' évolution de ces indicateurs motivationnels avec le test t de Student, et calculé la taille d' effet en de changement significatif avec le d de Cohen, dans l' échantillon total, et dans sous-échantillons ayant suivi chaque parcours. Notre objectif était d' étudier l' effet du programme sur les sentiments de d' et préparation. Discussion Nos résultats sont à nuancer par le fait que les analyses sont par nature réalisées sur les utilisateurs ayant terminé le programme. Or, une attrition importantemais attendueest observée au long du programme, nécessite d' être investiguée plus avant dans une étude à venir. Par ailleurs, s' agissant d'une étude retrospective observationnelle, l' absence de groupe contrôle ne permet pas de distinguer l' liée au programme de l' évolution naturelle. Cependant, le groupe au parcours raccourci peut servir de premier comparateur du groupe au parcours Cette recherche a été financée par une convention financière dans le cadre de "Territoires de Santé de Demain" par l'Eurométropole de Strasbourg sous la référence TSD-AMI2-2.13, et le contrat de thèse de Luz Adriana BUSTAMANTE, psychologue TCC, est financé par l'Agence de la Recherche et de la Technologie (ANRT), sous la CIFRE : 2019/0078.