Abstract 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