no code implementations • 15 Feb 2022 • Andreanne Lemay, Charley Gros, Enamundram Naga Karthik, Julien Cohen-Adad
Each label fusion method is studied using both the conventional training framework and the recently published SoftSeg framework that limits information loss by treating the segmentation task as a regression.
1 code implementation • 13 Oct 2021 • Nilser J. Laines Medina, Charley Gros, Julien Cohen-Adad, Virginie Callot, Arnaud Le Troter
The spinal cord (SC), which conveys information between the brain and the peripheral nervous system, plays a key role in various neurological disorders such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS), in which both gray matter (GM) and white matter (WM) may be impaired.
1 code implementation • 12 Sep 2021 • Uzay Macar, Enamundram Naga Karthik, Charley Gros, Andréanne Lemay, Julien Cohen-Adad
This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion Segmentation.
no code implementations • 5 May 2021 • Olivier Vincent, Charley Gros, Julien Cohen-Adad
While multiple studies have explored the relation between inter-rater variability and deep learning model uncertainty in medical segmentation tasks, little is known about the impact of individual rater style.
no code implementations • 18 Feb 2021 • Andreanne Lemay, Charley Gros, Olivier Vincent, Yaou Liu, Joseph Paul Cohen, Julien Cohen-Adad
This metadata is usually disregarded by image segmentation methods.
no code implementations • 23 Dec 2020 • Andreanne Lemay, Charley Gros, Zhizheng Zhuo, Jie Zhang, Yunyun Duan, Julien Cohen-Adad, Yaou Liu
To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation.
no code implementations • 18 Nov 2020 • Charley Gros, Andreanne Lemay, Julien Cohen-Adad
SoftSeg produces consistent soft predictions at tissues' interfaces and shows an increased sensitivity for small objects.
1 code implementation • 20 Oct 2020 • Charley Gros, Andreanne Lemay, Olivier Vincent, Lucas Rouhier, Anthime Bucquet, Joseph Paul Cohen, Julien Cohen-Adad
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data.
1 code implementation • 9 Mar 2020 • Olivier Vincent, Charley Gros, Joseph Paul Cohen, Julien Cohen-Adad
Despite recent improvements in medical image segmentation, the ability to generalize across imaging contrasts remains an open issue.
2 code implementations • 16 May 2018 • Charley Gros, Benjamin De Leener, Atef Badji, Josefina Maranzano, Dominique Eden, Sara M. Dupont, Jason Talbott, Ren Zhuoquiong, Yaou Liu, Tobias Granberg, Russell Ouellette, Yasuhiko Tachibana, Masaaki Hori, Kouhei Kamiya, Lydia Chougar, Leszek Stawiarz, Jan Hillert, Elise Bannier, Anne Kerbrat, Gilles Edan, Pierre Labauge, Virginie Callot, Jean Pelletier, Bertrand Audoin, Henitsoa Rasoanandrianina, Jean-Christophe Brisset, Paola Valsasina, Maria A. Rocca, Massimo Filippi, Rohit Bakshi, Shahamat Tauhid, Ferran Prados, Marios Yiannakas, Hugh Kearney, Olga Ciccarelli, Seth Smith, Constantina Andrada Treaba, Caterina Mainero, Jennifer Lefeuvre, Daniel S. Reich, Govind Nair, Vincent Auclair, Donald G. McLaren, Allan R. Martin, Michael G. Fehlings, Shahabeddin Vahdat, Ali Khatibi, Julien Doyon, Timothy Shepherd, Erik Charlson, Sridar Narayanan, Julien Cohen-Adad
The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data.