KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation.

Accurate segmentation of knee cartilage and meniscus in magnetic resonance imaging (MRI) is essential for the early detection and monitoring of complications such as cartilage erosion and osteoarthritis. Yet, manual annotation remains time-consuming, subjective, and inefficient for routine clinical use. In this study, we introduced KneeXNet-2.5D, a clinically oriented and explainable deep learning framework for accurate and efficient knee cartilage and meniscus segmentation in sagittal MRIs. Unlike traditional 3D segmentation methods, the proposed model employs a 2.5D architecture to capture inter-slice spatial context, achieving high segmentation accuracy while maintaining computational efficiency and optimal resource utilization. We further incorporated targeted image augmentation, including synthetic noise injection, to enhance the AI model robustness against medical imaging variability. The efficient design of the 2.5D model allows for reduced resource consumption, making it suitable for deployment in healthcare settings with limited computational infrastructure, particularly in low-resource hospitals and rural care environments. To enable open scientific research and ensure reproducibility, we constructed a gold-standard, manually segmented knee MRI dataset and publicly released it alongside the annotation guideline, source code, trained AI models, and a lightweight software application. An entropy-based AI explainability strategy was developed to highlight high-uncertainty regions that are most influential to model predictions, advancing transparency and interpretability. Clinical relevance and anatomical validity were further assessed through expert review by board-certified orthopedic surgeons. Together, these contributions demonstrate the AI model's anatomical fidelity, interpretability, and readiness for integration into musculoskeletal imaging workflows.