2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T
This work addresses the need for accurate spinal cord segmentation in neurological disorders like MS and ALS, but it is incremental as it adapts deep learning to a new imaging modality (7T MRI).
The study tackled the problem of segmenting gray and white matter in the cervical spinal cord from 7T MRI data, where existing methods for lower-field systems underperform, and proposed a new deep learning model with a specific data augmentation strategy, achieving robust multi-class segmentation for potential use in multi-center studies.
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. While automated methods for WM/GM segmentation are now largely available, these techniques, developed for conventional systems (3T or lower) do not necessarily perform well on 7T MRI data, which feature finer details, contrasts, but also different artifacts or signal dropout. The primary goal of this study is thus to propose a new deep learning model that allows robust SC/GM multi-class segmentation based on ultra-high resolution 7T T2*-w MR images. The second objective is to highlight the relevance of implementing a specific data augmentation (DA) strategy, in particular to generate a generic model that could be used for multi-center studies at 7T.