构建多视角半月板损伤分级数据集，提升MRI自动分析精度
MeniMV: A Multi-view Benchmark for Meniscus Injury Severity Grading
- 设计双视图（矢状位+冠状位）标注体系，支持前后角半月板分层评级
- 涵盖3000例患者6000张图像，病理标注量超以往两倍以上
- 适合临床导向的骨科影像智能诊断研究者使用
精确评估半月板角撕裂程度对膝关节损伤诊断至关重要，但现有自动化MRI分析方法多依赖粗粒度的病例级标签或二分类，缺乏定位与严重程度信息。本文提出MeniMV，一个专为角部半月板损伤分级设计的多视角基准数据集。该数据集包含来自三家医疗中心的750名患者的3000例膝关节MRI检查，提供6000张共注册的矢状位与冠状位图像。每例检查均经首席骨科医生验证，对前、后角半月板分别标注四等级（0-3级）的严重程度。与以往数据集相比，MeniMV的病灶标注数据量超过两倍，并独特地保留了临床实践中必需的双视图诊断上下文。为验证其价值，我们基于多种先进的CNN与Transformer模型进行基准测试，实验结果建立强基线并揭示严重程度分级中的挑战，为未来自动化肌肉骨骼影像研究提供坚实基础。
原文摘要 · Abstract (English)
Precise grading of meniscal horn tears is critical in knee injury diagnosis but remains underexplored in automated MRI analysis. Existing methods often rely on coarse study-level labels or binary classification, lacking localization and severity information. In this paper, we introduce MeniMV, a multi-view benchmark dataset specifically designed for horn-specific meniscus injury grading. MeniMV comprises 3,000 annotated knee MRI exams from 750 patients across three medical centers, providing 6,000 co-registered sagittal and coronal images. Each exam is meticulously annotated with four-tier (grade 0-3) severity labels for both anterior and posterior meniscal horns, verified by chief orthopedic physicians. Notably, MeniMV offers more than double the pathology-labeled data volume of prior datasets while uniquely capturing the dual-view diagnostic context essential in clinical practice. To demonstrate the utility of MeniMV, we benchmark multiple state-of-the-art CNN and Transformer-based models. Our extensive experiments establish strong baselines and highlight challenges in severity grading, providing a valuable foundation for future research in automated musculoskeletal imaging.
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