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

TL;DR

This paper introduces MeniMV, a comprehensive multi-view MRI dataset with detailed meniscus injury severity labels, enabling improved automated diagnosis and benchmarking of severity grading models.

Contribution

The paper presents MeniMV, the first large-scale, multi-view, severity-labeled MRI dataset for meniscus injury grading, facilitating advanced research in automated knee injury assessment.

Findings

MeniMV contains 3,000 exams with detailed annotations.

State-of-the-art models establish strong baseline performance.

Challenges in severity grading are identified and discussed.

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.