Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using H\"older Divergence and Mutual Information-Enhanced Knowledge Transfer

TL;DR

This paper introduces a robust brain tumor segmentation method that effectively handles incomplete MRI modalities by leveraging Holder divergence and mutual information, achieving high accuracy even with missing data.

Contribution

The proposed framework is the first to integrate Holder divergence and mutual information for modality-aware brain tumor segmentation with incomplete MRI data.

Findings

Outperforms existing methods on BraTS datasets

Maintains high accuracy with missing modalities

Effectively quantifies prediction discrepancies

Abstract

Multimodal MRI provides critical complementary information for accurate brain tumor segmentation. However, conventional methods struggle when certain modalities are missing due to issues such as image quality, protocol inconsistencies, patient allergies, or financial constraints. To address this, we propose a robust single-modality parallel processing framework that achieves high segmentation accuracy even with incomplete modalities. Leveraging Holder divergence and mutual information, our model maintains modality-specific features while dynamically adjusting network parameters based on the available inputs. By using these divergence- and information-based loss functions, the framework effectively quantifies discrepancies between predictions and ground-truth labels, resulting in consistently accurate segmentation. Extensive evaluations on the BraTS 2018 and BraTS 2020 datasets demonstrate superior performance over existing methods in handling missing modalities.