MeniMV: A Multi-view Benchmark for Meniscus Injury Severity Grading

• URL: http://arxiv.org/abs/2512.18437v1
• Date: Sat, 20 Dec 2025 17:22:55 GMT
• Title: MeniMV: A Multi-view Benchmark for Meniscus Injury Severity Grading
• Authors: Shurui Xu, Siqi Yang, Jiapin Ren, Zhong Cao, Hongwei Yang, Mengzhen Fan, Yuyu Sun, Shuyan Li,
• Abstract summary: MeniMV comprises 3,000 annotated knee MRI exams from 750 patients across three medical centers.<n>Each exam is meticulously annotated with four-tier (grade 0-3) severity labels for both anterior and posterior meniscal horns.
• Score: 7.152945592798872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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