A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

ABSTRACT: Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would also greatly facilitate the study of brain-behavior relationships by eliminating the laborious step of having a human expert manually segment the lesion on each brain scan. We propose a multi-modal multi-path convolutional neural network system for automating stroke lesion segmentation. Our system has nine end-to-end UNets that take as input 2-dimensional (2D) slices and examines all three planes with three different normalizations. Outputs from these nine total paths are concatenated into a 3D volume that is then passed to a 3D convolutional neural network to output a final lesion mask. We trained and tested o

SUBMITTER: Xue Y 

PROVIDER: S-EPMC6931186 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature