Objectives Accurate and efficient knee cartilage and bone segmentation are necessary for
basic science, clinical trial, and clinical applications. This work tested a multi-stage …

Purpose To organize a multi-institute knee MRI segmentation challenge for characterizing
the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring …

Knee osteoarthritis (OA) is a degenerative joint disease that is prevalent in advancing age.
The pathology of OA disease is still unclear, and there are no effective interventions that can …

Purpose To describe and evaluate a new fully automated musculoskeletal tissue
segmentation method using deep convolutional neural network (CNN) and three …

We present a method for the automated segmentation of knee bones and cartilage from
magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape …

Objective To assess automatic segmentations for surface-based analysis of cartilage
morphology and composition on knee magnetic resonance (MR) images. Methods 2D and …

Morphological changes in knee cartilage subregions are valuable imaging‐based
biomarkers for understanding progression of osteoarthritis, and they are typically detected …

Background and objective Segmentation is a crucial step in multiple biomechanics and
orthopedics applications. The time-intensiveness and expertise requirements of medical …

In this paper, we review the state-of-the-art approaches for knee articular cartilage
segmentation from conventional techniques to deep learning (DL) based techniques. Knee …

The ability to efficiently and reproducibly generate subject-specific 3D models of bone and
soft tissue is important to many areas of musculoskeletal research. However, methodologies …