U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool

Pulkit Kumar; Pravin Nagar; Chetan Arora; Anubha Gupta

U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool

Computer Vision and Pattern Recognition 2018-06-13 v1

Authors: Pulkit Kumar , Pravin Nagar , Chetan Arora , Anubha Gupta

Abstract

Automated brain tissue segmentation into white matter (WM), gray matter (GM), and cerebro-spinal fluid (CSF) from magnetic resonance images (MRI) is helpful in the diagnosis of neuro-disorders such as epilepsy, Alzheimer's, multiple sclerosis, etc. However, thin GM structures at the periphery of cortex and smooth transitions on tissue boundaries such as between GM and WM, or WM and CSF pose difficulty in building a reliable segmentation tool. This paper proposes a Fully Convolutional Neural Network (FCN) tool, that is a hybrid of two widely used deep learning segmentation architectures SegNet and U-Net, for improved brain tissue segmentation. We propose a skip connection inspired from U-Net, in the SegNet architetcure, to incorporate fine multiscale information for better tissue boundary identification. We show that the proposed U-SegNet architecture, improves segmentation performance, as measured by average dice ratio, to 89.74% on the widely used IBSR dataset consisting of T-1 weighted MRI volumes of 18 subjects.

Keywords

brain imaging brain tumor segmentation image segmentation

Cite

@article{arxiv.1806.04429,
  title  = {U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool},
  author = {Pulkit Kumar and Pravin Nagar and Chetan Arora and Anubha Gupta},
  journal= {arXiv preprint arXiv:1806.04429},
  year   = {2018}
}

Comments

Accepted in ICIP, 2018

U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool

Abstract

Keywords

Cite

Comments

Related papers