  The 3D morphology and quantitative assessment of knee articular cartilages
(i.e., femoral, tibial, and patellar cartilage) in magnetic resonance (MR)
imaging is of great importance for knee radiographic osteoarthritis (OA)
diagnostic decision making. However, effective and efficient delineation of all
the knee articular cartilages in large-sized and high-resolution 3D MR knee
data is still an open challenge. In this paper, we propose a novel framework to
solve the MR knee cartilage segmentation task. The key contribution is the
adversarial learning based collaborative multi-agent segmentation network. In
the proposed network, we use three parallel segmentation agents to label
cartilages in their respective region of interest (ROI), and then fuse the
three cartilages by a novel ROI-fusion layer. The collaborative learning is
driven by an adversarial sub-network. The ROI-fusion layer not only fuses the
individual cartilages from multiple agents, but also backpropagates the
training loss from the adversarial sub-network to each agent to enable joint
learning of shape and spatial constraints. Extensive evaluations are conducted
on a dataset including hundreds of MR knee volumes with diverse populations,
and the proposed method shows superior performance.
