Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks.

ABSTRACT:

Purpose

To develop a fast and accurate convolutional neural network based method for segmentation of thalamic nuclei.

Methods

A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization prepared rapid gradient echo (MPRAGE) data. A single network was optimized to work with images from healthy controls and patients with multiple sclerosis (MS) and essential tremor (ET), acquired at both 3 T and 7 T field strengths. WMn-MPRAGE images were manually delineated by a trained neuroradiologist using the Morel histological atlas as a guide to generate reference ground truth labels. Dice similarity coefficient and volume similarity index (VSI) were used to evaluate performanc

SUBMITTER: Majdi MS 

PROVIDER: S-EPMC7530079 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature