Automatic meniscus segmentation using cascaded deep convolutional neural networks with 2D conditional random fields in knee MR images

2020 ◽  
Author(s):  
UJU JEON ◽  
HYEONJIN KIM ◽  
HELEN HONG ◽  
JOONHO WANG
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Mr Images ◽  
Deep Convolutional Neural Networks

scholarly journals Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Cem M. Deniz ◽  
Siyuan Xiang ◽  
R. Spencer Hallyburton ◽  
Arakua Welbeck ◽  
James S. Babb ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Proximal Femur ◽  
Mr Images ◽  
Deep Convolutional Neural Networks

scholarly journals Automatic diagnosis of disc bulge and disc herniation based on lumbar MR images using deep convolutional neural networks (Preprint)

2019 ◽  
Author(s):  
Qiong Pan ◽  
Xiyang Liu ◽  
Kai Zhang ◽  
Lin He ◽  
Zhou Dong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
Disc Herniation ◽  
Lumbar Disc ◽  
Diagnostic Efficiency ◽  
Mr Images ◽  
Deep Convolutional Neural Networks ◽  
Automatic Diagnosis ◽  
Disc Bulge

BACKGROUND Lumbar abnormalities often lead to the lower back pain which has keep plaguing people’s lives. Magnetic resonance imaging (MRI) is one of the most efficient techniques to detect lumbar diseases and widely used in clinic. How to interpret massive amounts of magnetic resonance (MR) images quickly and accurately is an urgent problem. OBJECTIVE The aim of this study is to present an automatic system to diagnosis of disc bulge and herniation which is time-saving and effective so that can reduce radiologists’ workload. METHODS The diagnosis of disorders of lumbar vertebral is highly dependent on medical images, therefore, we choose two most common diseases disc bulge and herniation as the research objects. The study is mainly about classification of the axial lumbar disc MR images using deep convolutional neural networks. RESULTS This system comprises of four steps. First step, automatic localizes vertebral bodies (including L1, L2, L3, L4, L5, and S1, L: Lumbar, S: Sacral) in sagittal images using the Faster R-CNN and 4-fold cross-validations show 100% accuracy respectively. Second step, automatically determine the corresponding disc of each axial lumbar disc MR images with 100% accuracy. In the third step, the accuracy to automatic localizes intervertebral disc region of interest (ROI) in axial MR images is 100%. The three classification (disc normal, disc bulge and disc herniation) accuracies in the last step in five groups (L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1) are 92.7%, 84.4%, 92.1%, 90.4% and 84.2% respectively. CONCLUSIONS The automatic diagnosis system was successful built which can classify images of disc normal, disc bulge and disc herniation. This system provides an online test to interpret lumbar disc MR images which will be very helpful in improving the diagnostic efficiency and standardizing diagnosis reports, also, the system can be promoted to detect other lumbar abnormalities and cervical spondylosis.

scholarly journals Diagnostic interchangeability of deep convolutional neural networks reconstructed knee MR images: preliminary experience

2020 ◽  
Vol 10 (9) ◽  
pp. 1748-1762
Author(s):  
Naveen Subhas ◽  
Hongyu Li ◽  
Mingrui Yang ◽  
Carl S. Winalski ◽  
Joshua Polster ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Mr Images ◽  
Deep Convolutional Neural Networks ◽  
Reconstructed Knee
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