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Central China Normal University, Huazhong University of Science and Technology, Guangzhou University of Chinese Medicine and its First Affiliated Hospital, and the University of North Carolina at Chapel Hill propose a multimodal MRI segmentation network, CoReFuse-Med. The method uses LACE+IMSH to remove resampling artifacts at shallow layers and MBCF to calibrate modality contributions at deep layers, addressing the problem of low-quality modalities contaminating the fusion. On the EPVS dataset, it achieves a DSC of 0.7326 and an HD95 of 7.35, outperforming MedNeXt's 0.7085 and 8.17; on BraTS, its DSC is 0.8528 with only 2.665M parameters. The method targets the resolution degradation after spatial alignment and does not cover scenarios with missing modalities or strong noise.
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Oct 19
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Central China Normal University proposes CoReFuse-Med: purify first, then fuse, to address low-quality modalities dragging down multimodal MRI segmentation
A team from Central China Normal University and others proposed the multimodal MRI segmentation network CoReFuse-Med, which uses LACE+IMSH to remove resampling artifacts at shallow layers and MBCF to calibrate modality contributions at deep layers, solving the problem of low-quality modalities contaminating fusion. On the EPVS dataset, it achieves a DSC of 0.7326 and an HD95 of 7.35, outperforming MedNeXt's 0.7085 and 8.17; on BraTS, the DSC is 0.8528, with only 2.665M parameters. This method targets the resolution degradation after spatial alignment and does not cover scenarios with missing modalities or strong noise.