Magnetic resonance in medicine · 2026
Multi-label Ensemble Model for Knee Joint Anatomy and Lesion Segmentation: Segmentation of Clinical Images With Ensembling, Morphology, and Attention (SCEMA).
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Yu Andrew Seohwan, Yang Mingrui, Nakamura Kunio, Lartey Richard, Tosun Sercan, Subhas Naveen, Winalski Carl, Chaudhary Vipin, Li Xiaojuan
Аннотация
PURPOSE: Quantitative MRI analysis holds significant promise for improving the early diagnosis and prognosis of osteoarthritis (OA), where accurate tissue and lesion segmentation is a critical step for reliable quantification. In particular, bone marrow edema-like lesions (BMELs) are associated with OA, but accurate segmentation methods remain elusive.
METHODS: A fully automated pipeline - Segmentation of Clinical images with Ensembling, Morphology, and Attention (SCEMA) - was developed for segmenting bone, cartilage, and BMEL from standard clinical knee MRI. SCEMA leveraged a UNet-based network enhanced with an added attention mechanism, model ensembling, and morphological postprocessing. A range of network architectures, model configurations, and post-processing pipelines were evaluated to find the optimal model. Statistical analyses on the segmentation annotations were also conducted, assessing the size and quality of BMEL lesions.
RESULTS: The model achieved testing Dice similarity coefficients (DSC) of 0.684, 0.966, 0.830, 0.956, and 0.770 for BMEL, femur bone, femoral cartilage, tibia bone, and tibial cartilage respectively. For BMEL, the model performance was found to be more strongly correlated with BMEL quality than size.
CONCLUSION: Fully automatic, high-quality anatomical and lesion segmentation was attained from standard clinical knee MRI. For reproducibility, BMEL quality labels and segmentation annotations were shared with the public.
METHODS: A fully automated pipeline - Segmentation of Clinical images with Ensembling, Morphology, and Attention (SCEMA) - was developed for segmenting bone, cartilage, and BMEL from standard clinical knee MRI. SCEMA leveraged a UNet-based network enhanced with an added attention mechanism, model ensembling, and morphological postprocessing. A range of network architectures, model configurations, and post-processing pipelines were evaluated to find the optimal model. Statistical analyses on the segmentation annotations were also conducted, assessing the size and quality of BMEL lesions.
RESULTS: The model achieved testing Dice similarity coefficients (DSC) of 0.684, 0.966, 0.830, 0.956, and 0.770 for BMEL, femur bone, femoral cartilage, tibia bone, and tibial cartilage respectively. For BMEL, the model performance was found to be more strongly correlated with BMEL quality than size.
CONCLUSION: Fully automatic, high-quality anatomical and lesion segmentation was attained from standard clinical knee MRI. For reproducibility, BMEL quality labels and segmentation annotations were shared with the public.