Monitoring morphometric drift in lifelong learning segmentation of the spinal cord.

ABSTRACT: Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and injuries affecting the spinal cord. For instance, the spinal cord cross-sectional area can be used to monitor cord atrophy in multiple sclerosis and to characterize compression in degenerative cervical myelopathy. While robust, automatic segmentation methods to a wide variety of contrasts and pathologies have been developed over the past few years, whether their predictions are stable as the model is updated using new datasets has not been assessed. This is particularly important for deriving normative values from healthy participants. In this study, we present a spinal cord segmentation model trained on a multisite (n=75) dataset, including 9 d

SUBMITTER: Karthik EN 

PROVIDER: S-EPMC12306831 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature