3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies.

ABSTRACT:

Background and purpose

Deep learning algorithms for segmentation of multiple sclerosis (MS) plaques generally require training on large datasets. This manuscript evaluates the effect of transfer learning from segmentation of another pathology to facilitate use of smaller MS-specific training datasets. That is, a model trained for detection of one type of pathology was re-trained to identify MS lesions and active demyelination.

Materials and methods

In this retrospective study using MRI exams from 149 patients spanning 4/18/2014 to 7/8/2021, 3D convolutional neural networks were trained with a variable number of manually-segmented MS studies. Models were trained for FLAIR lesion segmentation at a single timepoint, new FLAIR lesion segmentation comparing two timepoints, and en

SUBMITTER: Wahlig SG 

PROVIDER: S-EPMC10641790 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature