Fully automated segmentation of brain tumor from multiparametric MRI using 3D context deep supervised U-Net.

ABSTRACT:

Purpose

Owing to histologic complexities of brain tumors, its diagnosis requires the use of multimodalities to obtain valuable structural information so that brain tumor subregions can be properly delineated. In current clinical workflow, physicians typically perform slice-by-slice delineation of brain tumor subregions, which is a time-consuming process and also more susceptible to intra- and inter-rater variabilities possibly leading to misclassification. To deal with this issue, this study aims to develop an automatic segmentation of brain tumor in MR images using deep learning.

Method

In this study, we develop a context deep-supervised U-Net to segment brain tumor subregions. A context block which aggregates multiscale contextual information for dense segmentation was pro

SUBMITTER: Lin M 

PROVIDER: S-EPMC11752426 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature