TY - GEN
T1 - Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation
AU - Tan, Chaowei
AU - Yan, Zhennan
AU - Zhang, Shaoting
AU - Li, Kang
AU - Metaxas, Dimitris N.
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Cartilage segmentation
KW - Collaborative multi-agent learning
UR - http://www.scopus.com/inward/record.url?scp=85075661466&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075661466&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32245-8_32
DO - 10.1007/978-3-030-32245-8_32
M3 - Conference contribution
AN - SCOPUS:85075661466
SN - 9783030322441
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 282
EP - 290
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -