This article presents an evaluation of biliary tract segmentation methods used for 3D reconstruction, which may be very usefull in various 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 their 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.
Published in | International Journal of Biomedical Engineering and Clinical Science (Volume 9, Issue 4) |
DOI | 10.11648/j.ijbecs.20230904.11 |
Page(s) | 66-74 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Segmentation, Biliary Tract, MRI Images, ERCP, U-Net
[1] | Conseil National Professionel d’Hépato-Gastroentérologie. Livre Blanc de l’Hépato-Gastroentérologie. www.cnp-hge.fr. |
[2] | Jean-Marc Dumonceau, Christine Kapral, Lars Aabakken, Ioannis S. Papanikolaou, Andrea Tringali, Geoffroy Vanbiervliet, Torsten Beyna, Mario Dinis-Ribeiro, Istvan Hritz, AlbertoMariani, Gregorios Paspatis, Franco Radaelli, Sundeep Lakhtakia, Andrew M. Veitch & Jeanin E. van Hooft J. (2020). ERCP-related adverse events: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy (52). doi: 10.1055/a-1075-4080. |
[3] | Aymeric Becq, Jérôme Szewczyk, Gregoire Salin, Marion Chartier, Ulriikka Chaput, Romain Leenhardt, Xavier Drayc, Lionel Arrived, & Marine Camus. (2023). ERCP 2.0: Biliary 3D-Reconstruction in Patients with Malignant Hilar Stricture. Clinics and Research in Hepatology and Gastroenterology (47). doi.org/10.1016/j.clinre.2023.102172. |
[4] | Marc H Goldfinger, Gerard R Ridgway, Carlos Ferreira, Caitlin R Langford, Lin Cheng, Arina Kazimianec, Andrea Borghetto, Thomas G Wright, Gary Woodward, Neelam Hassanali, Rowan C Nicholls, Hayley Simpson, Tom Waddell, Siddarth Vikal, Marija Mavar, Soubera Rymell, Ioan Wigley, Jaco Jacobs, Matt Kelly, Rajarshi Banerjee & J Michael Brady. (2020). Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges. Journal of Magnetic Resonance Imaging (52). doi: 10.1002/jmri.27113. |
[5] | Kevin Robinson & Paul F. Whelan. (2004). Segmentation of the Biliary Tree in MRCP Data. Proceedings of SPIE - The International Society for Optical Engineering (4877). doi: 10.1117/12.467438. |
[6] | George P. Ralli, Gerard R. Ridgway & Sir Michael Brady. (2020). Segmentation of the Biliary Tree from MRCP Images via the Monogenic Signal. Communications in Computer and Information Science (1248). doi: 10.1007/978-3-030-52791-4_9. |
[7] | Oleksandra V Ivashchenko, Erik-Jan Rijkhorst, Leon C Ter Beek, Nikie J Hoetjes, Bas Pouw, Jasper Nijkamp, Koert F D Kuhlmann & Theo J M Ruers. (2020). A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images. Magn Reson Imaging (68). doi: 10.1016/j.mri.2019.12.008. |
[8] | Mohammad Atallah Al-Oudat, Saleh Alomari, Hazem Qattous, Mohammad Azzeh & Tariq Al-Munaizel. (2021). An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision. International Journal of Computers, Communications and Control (16). doi: 10.15837/ijccc.2021.5.4275. |
[9] | Diana Nuñez-Ramirez, David Mata-Mendoza & Manuel Cedillo-Hernandez. (2022). Improving preprocessing in reversible data hiding based on contrast enhancement. Journal of King Saud University - Computer and Information Sciences (34). doi: 10.1016/j.jksuci.2021.05.007. |
[10] | Srinivasan Perumal & Thambusamy Velmurugan. (2018). Preprocessing by Contrast Enhancement Techniques for Medical Images. International Journal of Pure and Applied Mathematics (118). |
[11] | N. Senthilkumaran & S. Vaithegi. (2016). Image Segmentation By Using Thresholding Techniques For Medical Images. Computer Science & Engineering: An International Journal (6). doi: 1-13. 10.5121/cseij.2016.6101. |
[12] | Rafael C. Gonzalez & Richard E. Woods. (2008). Digital Image Processing. Third edition. PHI publication. |
[13] | J. Sauvola & M. Pietikainen. (2000). Adaptive document image binarization. Pattern Recognition (33). doi.org/10.1016/S0031-3203(99)00055-2 |
[14] | Korsuk Sirinukunwattana, Josien P. W. Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J. Matuszewski, Elia Bruni, Urko Sanchez, Anton Böhm, Olaf Ronneberger, BassemBen Cheikh, Daniel Racoceanu, Philipp Kainz, Michael Pfeiffer, Martin Urschler, David R. J. Snead & Nasir M. Rajpoot. (2017). Gland segmentation in colon histology images: The glas challenge contest. Medical Image Analysis (35). doi: 10.1016/j.media.2016.08.008. |
[15] | D. Karimi & S. E. Salcudean. (2020). Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks. IEEE Transactions on Medical Imaging (39). doi: 10.1109/TMI.2019.2930068. |
[16] | Ying-Hwey Nai, Bernice W. Teo, Nadya L. Tan, Sophie O'Doherty, Mary C. Stephenson, Yee Liang Thian, Edmund Chiong & Anthonin Reilhac. (2021). Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset. Computers in Biology and Medicine (134). doi: 10.1016/j.compbiomed.2021.104497. |
[17] | O. Ronneberger, P. Fischer & T. Brox. (2015). U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (9351). doi.org/10.48550/arXiv.1505.04597 |
[18] | X. Liu, L. Song, S. Liu & Y. Zhang. (2021). A Review of Deep-Learning-Based Medical Image Segmentation Methods. Sustainability (13). doi.org/10.3390/su13031224. |
APA Style
Essamlali, A., Millot-Maysounabe, V., Chartier, M., Salin, G., Becq, A., et al. (2023). Bile Duct Segmentation Methods Under 3D Slicer Applied to ERCP: Advantages and Disadvantages. International Journal of Biomedical Engineering and Clinical Science, 9(4), 66-74. https://doi.org/10.11648/j.ijbecs.20230904.11
ACS Style
Essamlali, A.; Millot-Maysounabe, V.; Chartier, M.; Salin, G.; Becq, A., et al. Bile Duct Segmentation Methods Under 3D Slicer Applied to ERCP: Advantages and Disadvantages. Int. J. Biomed. Eng. Clin. Sci. 2023, 9(4), 66-74. doi: 10.11648/j.ijbecs.20230904.11
AMA Style
Essamlali A, Millot-Maysounabe V, Chartier M, Salin G, Becq A, et al. Bile Duct Segmentation Methods Under 3D Slicer Applied to ERCP: Advantages and Disadvantages. Int J Biomed Eng Clin Sci. 2023;9(4):66-74. doi: 10.11648/j.ijbecs.20230904.11
@article{10.11648/j.ijbecs.20230904.11, author = {Abdelhadi Essamlali and Vincent Millot-Maysounabe and Marion Chartier and Grégoire Salin and Aymeric Becq and Lionel Arrivé and Marine Duboc Camus and Jérôme Szewczyk and Isabelle Claude}, title = {Bile Duct Segmentation Methods Under 3D Slicer Applied to ERCP: Advantages and Disadvantages}, journal = {International Journal of Biomedical Engineering and Clinical Science}, volume = {9}, number = {4}, pages = {66-74}, doi = {10.11648/j.ijbecs.20230904.11}, url = {https://doi.org/10.11648/j.ijbecs.20230904.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbecs.20230904.11}, abstract = {This article presents an evaluation of biliary tract segmentation methods used for 3D reconstruction, which may be very usefull in various 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 their 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. }, year = {2023} }
TY - JOUR T1 - Bile Duct Segmentation Methods Under 3D Slicer Applied to ERCP: Advantages and Disadvantages AU - Abdelhadi Essamlali AU - Vincent Millot-Maysounabe AU - Marion Chartier AU - Grégoire Salin AU - Aymeric Becq AU - Lionel Arrivé AU - Marine Duboc Camus AU - Jérôme Szewczyk AU - Isabelle Claude Y1 - 2023/11/09 PY - 2023 N1 - https://doi.org/10.11648/j.ijbecs.20230904.11 DO - 10.11648/j.ijbecs.20230904.11 T2 - International Journal of Biomedical Engineering and Clinical Science JF - International Journal of Biomedical Engineering and Clinical Science JO - International Journal of Biomedical Engineering and Clinical Science SP - 66 EP - 74 PB - Science Publishing Group SN - 2472-1301 UR - https://doi.org/10.11648/j.ijbecs.20230904.11 AB - This article presents an evaluation of biliary tract segmentation methods used for 3D reconstruction, which may be very usefull in various 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 their 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. VL - 9 IS - 4 ER -