Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures
#MRI brain segmentation #Report-supervised learning #MS-RSuper #Brain tumor analysis #Multimodal imaging #Medical AI #Lesion detection
📌 Key Takeaways
- Researchers developed MS-RSuper method for brain lesion segmentation using MRI reports
- The method addresses limitations in current report-supervised learning approaches
- MS-RSuper handles both quantitative and qualitative findings from radiology reports
- The approach outperformed existing methods on BraTS-MET/MEN datasets
📖 Full Retelling
Researchers Yubin Ge, Yongsong Huang, and Xiaofeng Liu introduced a novel method called MS-RSuper for brain lesion segmentation using multimodal MRI reports, addressing the challenge of limited dense voxel labels in medical imaging, in their paper submitted to arXiv on February 24, 2026. The research tackles a significant problem in medical image analysis where radiology reports often contain incomplete information, describing only the largest lesions with qualitative terms like 'mild' or 'possible' rather than precise voxel-level annotations. Current report-supervised learning methods struggle with these limitations, often over-constraining models or hallucinating unreported findings when processing complex multimodal MRI data with various substructures. The MS-RSuper method represents a breakthrough by explicitly parsing both global quantitative findings and modality-specific qualitative cues from radiology reports, creating a more accurate and efficient approach to brain tumor segmentation. The researchers validated their method on 1238 report-labeled BraTS-MET/MEN scans, demonstrating significant performance improvements over both sparsely-supervised baselines and naive report-supervised approaches. This innovation could potentially reduce the burden on radiologists while maintaining diagnostic accuracy in brain tumor analysis.
🏷️ Themes
Medical Imaging, Artificial Intelligence, Healthcare Technology
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Original Source
--> Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.20994 [Submitted on 24 Feb 2026] Title: Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures Authors: Yubin Ge , Yongsong Huang , Xiaofeng Liu View a PDF of the paper titled Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures, by Yubin Ge and Yongsong Huang and Xiaofeng Liu View PDF HTML Abstract: Report-supervised learning seeks to alleviate the need for dense tumor voxel labels with constraints derived from radiology reports (e.g., volumes, counts, sizes, locations). In MRI studies of brain tumors, however, we often involve multi-parametric scans and substructures. Here, fine-grained modality/parameter-wise reports are usually provided along with global findings and are correlated with different substructures. Moreover, the reports often describe only the largest lesion and provide qualitative or uncertain cues (``mild,'' ``possible''). Classical RSuper losses (e.g., sum volume consistency) can over-constrain or hallucinate unreported findings under such incompleteness, and are unable to utilize these hierarchical findings or exploit the priors of varied lesion types in a merged dataset. We explicitly parse the global quantitative and modality-wise qualitative findings and introduce a unified, one-sided, uncertainty-aware formulation (MS-RSuper) that: aligns modality-specific qualitative cues (e.g., T1c enhancement, FLAIR edema) with their corresponding substructures using existence and absence losses; enforces one-sided lower-bounds for partial quantitative cues (e.g., largest lesion size, minimal multiplicity iii) adds extra- vs. intra-axial anatomical priors to respect cohort differences. Certainty tokens scale penalties; missing cues are down-weighted. On 1238 report-labeled BraTS-MET/MEN scans, our MS-RSuper largely outperforms both a sparsely-supervised baseline and a naive RSuper method. Comments: IEEE I...
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