Convolutional Neural Networks Applied to Multiple Sclerosis Lesion Segmentation on 3D Brain Magnetic Resonance Images

2018 ◽  
Author(s):  
R. Naranjo ◽  
G. Ulloa ◽  
H. Allende-Cid ◽  
H. Allende
Keyword(s):  
Multiple Sclerosis ◽  
Neural Networks ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
Magnetic Resonance Images ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation

scholarly journals One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

2019 ◽  
Vol 21 ◽  
pp. 101638 ◽  
Author(s):  
Sergi Valverde ◽  
Mostafa Salem ◽  
Mariano Cabezas ◽  
Deborah Pareto ◽  
Joan C. Vilanova ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Domain Adaptation ◽  
Multiple Sclerosis Lesion ◽  
Lesion 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.

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