Automatic segmentation of spinal cord multiple sclerosis lesions across multiple sites, contrasts and vendors

Motivation: Longitudinal analysis of spinal cord multiple sclerosis (MS) lesions is clinically relevant for the early diagnosis and monitoring of MS progression. Goal(s): Develop a deep learning tool for the automatic segmentation of MS spinal cord lesions on PSIR and STIR images from multiple sites. Approach: A nnUNet model was trained and tested on the baseline data and applied to follow-up scans to create lesion distribution maps. Results: We demonstrated the utility of the model to map the spatio-temporal distribution of MS lesions across MS phenotypes. The model is packaged into an open-source software. Impact: Automatic segmentation of spinal cord lesions in large cohorts helps to identify signatures of MS phenotypes for ultimately improving prognosis and optimizing treatment for people with MS.

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
2024-11-26
DOI
https://doi.org/10.58530/2024/1395
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Automatic segmentation of spinal cord multiple sclerosis lesions across multiple sites, contrasts and vendors

Jiwon Oh, Julien Cohen‐Adad, Jan Valošek, Shannon Kolind 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
Brain Tumor Detection and Classification
article

Automatic segmentation of spinal cord multiple sclerosis lesions across multiple sites, contrasts and vendors

Jiwon Oh, Julien Cohen‐Adad, Jan Valošek, Shannon Kolind, Nathan Molinier, Anthony Traboulsee, Lisa Eunyoung Lee, Alexandre Prat, Roger C. Tam, Pierre‐Louis Benveniste, Michelle Chen, Zachary Vavasour
article en

Abstract

Motivation: Longitudinal analysis of spinal cord multiple sclerosis (MS) lesions is clinically relevant for the early diagnosis and monitoring of MS progression. Goal(s): Develop a deep learning tool for the automatic segmentation of MS spinal cord lesions on PSIR and STIR images from multiple sites. Approach: A nnUNet model was trained and tested on the baseline data and applied to follow-up scans to create lesion distribution maps. Results: We demonstrated the utility of the model to map the spatio-temporal distribution of MS lesions across MS phenotypes. The model is packaged into an open-source software. Impact: Automatic segmentation of spinal cord lesions in large cohorts helps to identify signatures of MS phenotypes for ultimately improving prognosis and optimizing treatment for people with MS.

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%
Brain Tumor Detection and Classification
DOISourceOpenAlex
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Automatic segmentation of spinal cord multiple sclerosis lesions across multiple sites, contrasts and vendors — Jiwon Oh, Julien Cohen‐Adad, 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 (2024) | TGRS Research Map | TGRS