EPISeg: Automatic Segmentation of Spinal Cord fMRI Data

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.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/0961
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
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article

EPISeg: Automatic Segmentation of Spinal Cord fMRI Data

Sean Mackey, Shahabeddin Vahdat, Dimitri Van De Ville, Jürgen Finsterbusch et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Medical Imaging and Analysis
article

EPISeg: Automatic Segmentation of Spinal Cord fMRI Data

Sean Mackey, Shahabeddin Vahdat, Dimitri Van De Ville, Jürgen Finsterbusch, Véronique Marchand‐Pauvert, Gary H. Glover, Julien Cohen‐Adad, J.C. Brooks, Yasin Y. Dhaher, Fauziyya Muhammad, Simon Schading‐Sassenhausen, Sonia Medina, Dario Pfyffer, Molly G. Bright, Benjamin De Leener, Jan Haaker, Kimberly J. Hemmerling, Christine Law, Merve Kaptan, Mahdi Mobarak-Abadi, Stephen B. McMahon, Patrick Freund, Kenneth A. Weber, Alice Dabbagh, Grace Haynes, Steven Williams, Yufen Chen, Falk Eippert, Zachary A. Smith, Gergely Dávid, Olivia S. Kowalczyk, Julien Doyon, Alexandra Tinnermann, Ali Khatibi, Ovidiu Lungu, Christian Buechel, Katherine T. Martucci, Mark A. Hoggarth, David J. Lythgoe, Todd B. Parrish, Nawal Kinany, Robert Barry, D. Rangaprakash, Dimitra Tsivaka, Christian W. Kündig, Matthew A. Howard, Rohan Banerjee
article en

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.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 76%
Medical Imaging and Analysis
DOIOpen Access PDFOpenAlex
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