Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network

TL;DR

This paper presents a deep convolutional neural network approach for accurate segmentation of glioma tumors in brain MRI images, addressing challenges like irregular shapes and ambiguous boundaries with novel architecture and training methods.

Contribution

Introduction of a new deep CNN architecture with inception modules and a two-phase weighted training method for improved glioma segmentation accuracy.

Findings

Enhanced segmentation performance on BRATS datasets

Effective handling of irregular tumor shapes

Reduction of false positives with morphological post-processing

Abstract

Detection of brain tumor using a segmentation based approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. The automation of brain tumor segmentation remains a challenging problem mainly due to significant variations in its structure. An automated brain tumor segmentation algorithm using deep convolutional neural network (DCNN) is presented in this paper. A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images. Recent developments in deep neural networks such as drop-out, batch normalization, non-linear activation and inception module are used to build a new ILinear nexus architecture. The module overcomes the over-fitting problem arising due to scarcity of data using drop-out regularizer. Images are normalized and bias field corrected in the pre-processing step and then extracted patches are passed through a DCNN, which assigns an output label to the central pixel of each patch. Morphological operators are used for post-processing to remove small false positives around the edges. A two-phase weighted training method is introduced and evaluated using BRATS 2013 and BRATS 2015 datasets, where it improves the performance parameters of state-of-the-art techniques under similar settings.