Evaluation of fully automated myocardial segmentation techniques in native and contrast‐enhanced T1‐mapping cardiovascular magnetic resonance images using fully convolutional neural networks

2020 ◽  
Author(s):  
Nadia A. Farrag ◽  
Aidan Lochbihler ◽  
James A. White ◽  
Eranga Ukwatta
Keyword(s):  
Neural Networks ◽  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
T1 Mapping ◽  
Magnetic Resonance Images ◽  
Fully Convolutional Neural Networks ◽  
Contrast Enhanced ◽  
Myocardial Segmentation

Myocardial segmentation in cardiac magnetic resonance images using fully convolutional neural networks

2018 ◽  
Vol 44 ◽  
pp. 48-57 ◽  
Author(s):  
Liset Vázquez Romaguera ◽  
Francisco Perdigón Romero ◽  
Cicero Ferreira Fernandes Costa Filho ◽  
Marly Guimarães Fernandes Costa
Keyword(s):  
Neural Networks ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
Magnetic Resonance Images ◽  
Fully Convolutional Neural Networks ◽  
Myocardial Segmentation

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.

KIKI ‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images

2018 ◽  
Vol 80 (5) ◽  
pp. 2188-2201 ◽  
Author(s):  
Taejoon Eo ◽  
Yohan Jun ◽  
Taeseong Kim ◽  
Jinseong Jang ◽  
Ho‐Joon Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
Magnetic Resonance Images ◽  
Cross Domain
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