Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images

TL;DR

This paper explores deep learning for brain tumor segmentation in MRI images, comparing single versus multiple network approaches, achieving improved accuracy with multiple networks.

Contribution

It introduces a multi-network approach for brain tumor segmentation and evaluates its effectiveness compared to a single network.

Findings

Dice score of 0.73 with a single network

Dice score of 0.79 with multiple networks

Multi-network approach improves segmentation accuracy

Abstract

Recently deep learning has been playing a major role in the field of computer vision. One of its applications is the reduction of human judgment in the diagnosis of diseases. Especially, brain tumor diagnosis requires high accuracy, where minute errors in judgment may lead to disaster. For this reason, brain tumor segmentation is an important challenge for medical purposes. Currently several methods exist for tumor segmentation but they all lack high accuracy. Here we present a solution for brain tumor segmenting by using deep learning. In this work, we studied different angles of brain MR images and applied different networks for segmentation. The effect of using separate networks for segmentation of MR images is evaluated by comparing the results with a single network. Experimental evaluations of the networks show that Dice score of 0.73 is achieved for a single network and 0.79 in obtained for multiple networks.