scholarly journals Atlas-based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks

2021 ◽  
pp. 14-23
Author(s):  
Jianing Wang ◽  
Dingjie Su ◽  
Yubo Fan ◽  
Srijata Chakravorti ◽  
Jack H. Noble ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Ct Images ◽  
Metal Artifact

Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images

2021 ◽  
Vol 104 ◽  
pp. 107185 ◽  
Author(s):  
Ying Da Wang ◽  
Mehdi Shabaninejad ◽  
Ryan T. Armstrong ◽  
Peyman Mostaghimi
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Ct Images ◽  
Micro Ct ◽  
2D And 3D

scholarly journals Automatic Aortic Valve Cusps Segmentation from CT Images Based on the Cascading Multiple Deep Neural Networks

2022 ◽  
Vol 8 (1) ◽  
pp. 11
Author(s):  
Gakuto Aoyama ◽  
Longfei Zhao ◽  
Shun Zhao ◽  
Xiao Xue ◽  
Yunxin Zhong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Aortic Valve ◽  
Deep Neural Networks ◽  
Spatial Configuration ◽  
Ct Images ◽  
Anatomical Landmarks ◽  
Strong Correlations ◽  
Coronary Cusp ◽  
The Right ◽  
Right Coronary Cusp

Accurate morphological information on aortic valve cusps is critical in treatment planning. Image segmentation is necessary to acquire this information, but manual segmentation is tedious and time consuming. In this paper, we propose a fully automatic aortic valve cusps segmentation method from CT images by combining two deep neural networks, spatial configuration-Net for detecting anatomical landmarks and U-Net for segmentation of aortic valve components. A total of 258 CT volumes of end systolic and end diastolic phases, which include cases with and without severe calcifications, were collected and manually annotated for each aortic valve component. The collected CT volumes were split 6:2:2 for the training, validation and test steps, and our method was evaluated by five-fold cross validation. The segmentation was successful for all CT volumes with 69.26 s as mean processing time. For the segmentation results of the aortic root, the right-coronary cusp, the left-coronary cusp and the non-coronary cusp, mean Dice Coefficient were 0.95, 0.70, 0.69, and 0.67, respectively. There were strong correlations between measurement values automatically calculated based on the annotations and those based on the segmentation results. The results suggest that our method can be used to automatically obtain measurement values for aortic valve morphology.

scholarly journals Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio

2021 ◽  
Author(s):  
Saman Fouladi ◽  
M.J. Ebadi ◽  
Ali A. Safaei ◽  
Mohd Yazid Bajuri ◽  
Ali Ahmadian
Keyword(s):  
Neural Networks ◽  
Software Defined Radio ◽  
Deep Neural Networks ◽  
Ct Images

Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN)

2019 ◽  
Vol 104 (4) ◽  
pp. 924-932 ◽  
Author(s):  
Chang Liu ◽  
Stephen J. Gardner ◽  
Ning Wen ◽  
Mohamed A. Elshaikh ◽  
Farzan Siddiqui ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Ct Images
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