Robust Divergence Learning for Missing-Modality Segmentation
This work addresses a clinically valuable issue for medical imaging by enabling robust segmentation despite missing data, though it appears incremental as it builds on existing multimodal approaches.
The paper tackles the problem of brain tumor segmentation with missing MRI modalities by introducing a single-modality parallel processing network framework using Hölder divergence and mutual information, achieving superior performance on BraTS 2018 and 2020 datasets compared to existing methods.
Multimodal Magnetic Resonance Imaging (MRI) provides essential complementary information for analyzing brain tumor subregions. While methods using four common MRI modalities for automatic segmentation have shown success, they often face challenges with missing modalities due to image quality issues, inconsistent protocols, allergic reactions, or cost factors. Thus, developing a segmentation paradigm that handles missing modalities is clinically valuable. A novel single-modality parallel processing network framework based on Hölder divergence and mutual information is introduced. Each modality is independently input into a shared network backbone for parallel processing, preserving unique information. Additionally, a dynamic sharing framework is introduced that adjusts network parameters based on modality availability. A Hölder divergence and mutual information-based loss functions are used for evaluating discrepancies between predictions and labels. Extensive testing on the BraTS 2018 and BraTS 2020 datasets demonstrates that our method outperforms existing techniques in handling missing modalities and validates each component's effectiveness.