Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation

TL;DR

This paper introduces a multi-modal CNN approach for brain tumor segmentation, exploring fusion methods to effectively combine different imaging modalities and improve segmentation accuracy.

Contribution

It adapts and evaluates fusion techniques from video recognition for brain tumor segmentation, demonstrating improved performance with separate modality representations.

Findings

Fusion methods enhance segmentation accuracy

Separate modality learning outperforms joint representations

Efficient multi-modal integration improves CNN performance

Abstract

In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for brain tumor segmentation. We investigate how to combine different modalities efficiently in the CNN framework.We adapt various fusion methods, which are previously employed on video recognition problem, to the brain tumor segmentation problem,and we investigate their efficiency in terms of memory and performance.Our experiments, which are performed on BRATS dataset, lead us to the conclusion that learning separate representations for each modality and combining them for brain tumor segmentation could increase the performance of CNN systems.