A comparison of manual and automated neural architecture search for white matter tract segmentation.

ABSTRACT: Segmentation of white matter tracts in diffusion magnetic resonance images is an important first step in many imaging studies of the brain in health and disease. Similar to medical image segmentation in general, a popular approach to white matter tract segmentation is to use U-Net based artificial neural network architectures. Despite many suggested improvements to the U-Net architecture in recent years, there is a lack of systematic comparison of architectural variants for white matter tract segmentation. In this paper, we evaluate multiple U-Net based architectures specifically for this purpose. We compare the results of these networks to those achieved by our own various architecture changes, as well as to new U-Net architectures designed automatically via neural architecture search (NA

SUBMITTER: Tchetchenian A 

PROVIDER: S-EPMC9884270 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature