EPISeg: Automatic Segmentation of Spinal Cord fMRI Data
Year: 2025
Type: conference-paper
Abstract: Motivation: Spinal cord fMRI holds promise for somatosensory and motor research, but challenges in data acquisition and preprocessing limit its potential. Goal(s): Develop an automated segmentation tool for spinal cord fMRI data that minimizes manual intervention and improves segmentation accuracy which is important for data analysis. Approach: We introduce EPISeg, a deep learning-based segmentation method trained on an open-source multi-site dataset of 406 subjects using active learning with human-in-the-loop feedback. Results: EPISeg outperformed established methods like PropSeg, DeepSeg, and a Contrast-agnostic model, achieving a Dice score of 0.88. Impact: EPISeg significantly enhances spinal fMRI research by enabling automated, accurate segmentations of EPI data, overcoming limitations of manual segmentation. Its integration into SCT broadens accessibility and reproducibility, facilitating robust group-level analyses essential for advancing studies of spinal processes and disorders.
DOI: 10.58530/2025/0961
Authors: Rohan Banerjee, Merve Kaptan, Alexandra Tinnermann, Ali Khatibi, Alice Dabbagh, Christian Buechel, Christian Kündig, Christine Law, Dario Pfyffer, David Lythgoe, Dimitra Tsivaka, Dimitri Van De Ville, Falk Eippert, Fauziyya Muhammad, Gary Glover, Gergely David, Grace Haynes, Jan Haaker, Jonathan Brooks, Julien Doyon, Jürgen Finsterbusch, Katherine Martucci, Kimberly Hemmerling, Mahdi Mobarak-Abadi, Mark Hoggarth, Matthew Howard, Molly Bright, Nawal Kinany, Olivia Kowalczyk, Ovidiu Lungu, Patrick Freund, Rangaprakash Deshpande, Robert Barry, Sean Mackey, Shahabeddin Vahdat, Simon Schading, Sonia Medina, Stephen McMahon, Steven Williams, Todd Parrish, Véronique Marchand-Pauvert, Yasin Dhaher, Yufen Chen, Zachary Smith, Kenneth Weber, Benjamin De Leener, Julien Cohen-Adad
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414238196
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