Segmentation of cartilage in knee MRI is an important process for various clinical tasks in
diagnosis and treatment planning of osteoarthritis. Recently, the deep segmentation …

Automated Cartilage segmentation is essential for improving the performance of advanced
Knee Osteoarthritis (OA) assessment due to its convoluted 3D structure. In this paper, we …

The 3D morphology and quantitative assessment of knee articular cartilages (ie, femoral,
tibial, and patellar cartilage) in magnetic resonance (MR) imaging is of great importance for …

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 cartilage and bone segmentation is critical for physicians to analyze and diagnose
articular damage and knee osteoarthritis (OA). Deep learning (DL) methods for medical …

MRI image segmentation of knee articular cartilage is important for the early diagnosis of
osteoarthritis. Unfortunately, manual segmentation of articular cartilage consumes a lot of …

Purpose Fully automatic tissue segmentation is an essential step to translate quantitative
MRI techniques to clinical setting. The goal of this study was to develop a novel approach …

Background This study aimed to build a deep learning model to automatically segment
heterogeneous clinical MRI scans by optimizing a pre-trained model built from a …

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