Segmenting Brain Tumors with Symmetry

TL;DR

This paper introduces a neural network method that encodes brain symmetry to improve brain tumor segmentation by focusing on asymmetries, which are more indicative of tumor regions, leading to more efficient information extraction.

Contribution

The paper proposes a novel approach to encode brain symmetry into neural networks, enhancing tumor segmentation performance by emphasizing asymmetries.

Findings

Symmetry encoding improves segmentation accuracy.

The method enhances information extraction efficiency.

Performance verified on a brain tumor dataset.

Abstract

We explore encoding brain symmetry into a neural network for a brain tumor segmentation task. A healthy human brain is symmetric at a high level of abstraction, and the high-level asymmetric parts are more likely to be tumor regions. Paying more attention to asymmetries has the potential to boost the performance in brain tumor segmentation. We propose a method to encode brain symmetry into existing neural networks and apply the method to a state-of-the-art neural network for medical imaging segmentation. We evaluate our symmetry-encoded network on the dataset from a brain tumor segmentation challenge and verify that the new model extracts information in the training images more efficiently than the original model.