TY - JOUR
T1 - KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities
AU - Liu, Tianyi
AU - Jiang, Haochuan
AU - Huang, Kaizhu
PY - 2025
Y1 - 2025
N2 - Magnetic resonance imaging (MRI), with modalities including T1, T2, T1ce, and Flair, providing complementary information critical for sub-region analysis, is widely used for brain tumor diagnosis. However, clinical practice often suffers from varying degrees of incompleteness of necessary modalities due to reasons such as susceptibility to artifacts. It significantly impairs segmentation model performance. Given the limited available modalities at hand, existing approaches attempt to project them into a shared latent space. However, they ignore decomposing the modality-shared and modality-specific information and failed to construct the relationship among different modalities. Such deficiency limits the effectiveness of the segmentation performance, particularly at the time when the amount of data in each modality is different. In this paper, we propose the plug-and-play Koopman Multi-modality Decomposition (KMD) module, leveraging the Koopman Invariant Subspace to disentangle modality-common and modality-specific information. It is capable of constructing modality relationships that minimize bias toward modalities across various modalityincomplete scenarios. More importantly, it can be integrated into several existing backbones feasibility. Through theoretical deductions and extensive empirical experiences on the BraTS2018 and BraTS2020 datasets, we have sufficiently demonstrated the effectiveness of the proposed KMD to promote generalization performance.
AB - Magnetic resonance imaging (MRI), with modalities including T1, T2, T1ce, and Flair, providing complementary information critical for sub-region analysis, is widely used for brain tumor diagnosis. However, clinical practice often suffers from varying degrees of incompleteness of necessary modalities due to reasons such as susceptibility to artifacts. It significantly impairs segmentation model performance. Given the limited available modalities at hand, existing approaches attempt to project them into a shared latent space. However, they ignore decomposing the modality-shared and modality-specific information and failed to construct the relationship among different modalities. Such deficiency limits the effectiveness of the segmentation performance, particularly at the time when the amount of data in each modality is different. In this paper, we propose the plug-and-play Koopman Multi-modality Decomposition (KMD) module, leveraging the Koopman Invariant Subspace to disentangle modality-common and modality-specific information. It is capable of constructing modality relationships that minimize bias toward modalities across various modalityincomplete scenarios. More importantly, it can be integrated into several existing backbones feasibility. Through theoretical deductions and extensive empirical experiences on the BraTS2018 and BraTS2020 datasets, we have sufficiently demonstrated the effectiveness of the proposed KMD to promote generalization performance.
M3 - Conference article
JO - IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
JF - IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
ER -