Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using Hölder Divergence and Mutual Information-Enhanced Knowledge Transfer
Journal:
arXiv
Published Date:
Jul 2, 2025
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.
