New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images.

ABSTRACT: Multiple sclerosis (MS) is an autoimmune disease that causes lesions in the central nervous system of humans due to demyelinating axons. Magnetic resonance imaging (MRI) is widely used for monitoring and measuring MS lesions. Automated methods for MS lesion segmentation have usually been performed on individual MRI scans. Recently, tracking lesion activity for quantifying and monitoring MS disease progression, especially detecting new lesions, has become an important biomarker. In this study, a unique pipeline with a deep neural network that combines U-Net, attention gate, and residual learning is proposed to perform better new MS lesion segmentation using baseline and follow-up 3D FLAIR MR images. The proposed network has a similar architecture to U-Net and is formed from residual units w

SUBMITTER: Sarica B 

PROVIDER: S-EPMC9365701 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature