Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using Hölder Divergence and Mutual Information-Enhanced Knowledge Transfer
This addresses a critical issue in medical imaging for clinicians and patients, where missing modalities due to practical constraints can hinder accurate diagnosis, though it is incremental as it builds on existing knowledge transfer techniques.
The paper tackles the problem of brain tumor segmentation with incomplete MRI modalities by proposing a robust single-modality parallel processing framework, achieving superior performance on BraTS 2018 and 2020 datasets compared to existing methods.
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.