3-D segmentation of articular cartilages by graph cuts using knee MR images from osteoarthritis initiative

2008 ◽  
Author(s):  
Hackjoon Shim ◽  
Soochan Lee ◽  
Bohyeong Kim ◽  
Cheng Tao ◽  
Samuel Chang ◽  
...  
Keyword(s):  
Graph Cuts ◽  
Mr Images ◽  
Osteoarthritis Initiative ◽  
Articular Cartilages

Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts

NeuroImage ◽  
2008 ◽  
Vol 43 (4) ◽  
pp. 708-720 ◽  
Author(s):  
Fedde van der Lijn ◽  
Tom den Heijer ◽  
Monique M.B. Breteler ◽  
Wiro J. Niessen
Keyword(s):  
Graph Cuts ◽  
Mr Images ◽  
Atlas Registration ◽  
Hippocampus Segmentation ◽  
Voxel Classification

scholarly journals Segmentation of abdomen MR images using kernel graph cuts with shape priors

2013 ◽  
Vol 12 (1) ◽  
pp. 124 ◽  
Author(s):  
Qing Luo ◽  
Wenjian Qin ◽  
Tiexiang Wen ◽  
Jia Gu ◽  
Nikolas Gaio ◽  
...  
Keyword(s):  
Graph Cuts ◽  
Shape Priors ◽  
Mr Images

scholarly journals Natural History of Intrameniscal Signal Intensity on Knee MR Images: Six Years of Data from the Osteoarthritis Initiative

Radiology ◽  
2016 ◽  
Vol 278 (1) ◽  
pp. 164-171 ◽  
Author(s):  
Jaanika Kumm ◽  
Frank W. Roemer ◽  
Ali Guermazi ◽  
Aleksandra Turkiewicz ◽  
Martin Englund
Keyword(s):  
Natural History ◽  
Signal Intensity ◽  
Mr Images ◽  
History Of ◽  
Osteoarthritis Initiative

scholarly journals Comparative Study of Encoder–Decoder-Based Convolutional Neural Networks in Cartilage Delineation from Knee Magnetic Resonance Images

2020 ◽  
Vol 16 ◽  
Author(s):  
Ching Wai Yong ◽  
Khin Wee Lai ◽  
Belinda Pingguan Murphy ◽  
Yan Chai Hum
Keyword(s):  
Neural Networks ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
Soft Tissues ◽  
Early Stage ◽  
Joint Inflammation ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
Daily Lives ◽  
Osteoarthritis Initiative

Background: Osteoarthritis (OA) is a common degenerative joint inflammation which may lead to disability. Although OA is not lethal, this disease will remarkably affect patient’s mobility and their daily lives. Detecting OA at an early stage allows for early intervention and may slow down disease progression. Introduction: Magnetic resonance imaging is a useful technique to visualize soft tissues within the knee joint. Cartilage delineation in magnetic resonance (MR) images helps in understanding the disease progressions. Convolutional neural networks (CNNs) have shown promising results in computer vision tasks, and various encoder–decoder-based segmentation neural networks are introduced in the last few years. However, the performances of such networks are unknown in the context of cartilage delineation. Methods: This study trained and compared 10 encoder–decoder-based CNNs in performing cartilage delineation from knee MR images. The knee MR images are obtained from Osteoarthritis Initiative (OAI). The benchmarking process is to compare various CNNs based on the physical specifications and segmentation performances. Results: LadderNet has the least trainable parameters with model size of 5 MB. UNetVanilla crowned the best performances by having 0.8369, 0.9108, and 0.9097 on JSC, DSC, and MCC. Conclusion: UNetVanilla can be served as a benchmark for cartilage delineation in knee MR images while LadderNet served as alternative if there are hardware limitations during production.

Measuring atrophy by simultaneous segmentation of serial MR images using 4-D graph-cuts

2010 ◽  
Author(s):  
Robin Wolz ◽  
Rolf A. Heckemann ◽  
Paul Aljabar ◽  
Joseph V. Hajnal ◽  
Alexander Hammers ◽  
...  
Keyword(s):  
Graph Cuts ◽  
Mr Images

Fully automated subchondral bone segmentation from knee MR images: Data from the Osteoarthritis Initiative

2017 ◽  
Vol 88 ◽  
pp. 110-125 ◽  
Author(s):  
Akash Gandhamal ◽  
Sanjay Talbar ◽  
Suhas Gajre ◽  
Ruslan Razak ◽  
Ahmad Fadzil M. Hani ◽  
...  
Keyword(s):  
Subchondral Bone ◽  
Bone Segmentation ◽  
Mr Images ◽  
Osteoarthritis Initiative
Sign in / Sign up

Export Citation Format

Share Document