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 Boosting Resolution and Recovering Texture of 2D and 3D Micro‐CT Images with Deep Learning

2020 ◽  
Vol 56 (1) ◽  
Author(s):  
Ying Da Wang ◽  
Ryan T. Armstrong ◽  
Peyman Mostaghimi
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Micro Ct ◽  
2D And 3D

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

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

scholarly journals An Application of Deep Neural Networks for Segmentation of Microtomographic Images of Rock Samples

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 72 ◽  
Author(s):  
Igor Varfolomeev ◽  
Ivan Yakimchuk ◽  
Ilia Safonov
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Ground Truth ◽  
Indicator Kriging ◽  
Generalization Capability ◽  
Automatic Mode ◽  
Fine Structures ◽  
Segmentation Of Images ◽  
2D And 3D ◽  
Single Set

Image segmentation is a crucial step of almost any Digital Rock workflow. In this paper, we propose an approach for generation of a labelled dataset and investigate an application of three popular convolutional neural networks (CNN) architectures for segmentation of 3D microtomographic images of samples of various rocks. Our dataset contains eight pairs of images of five specimens of sand and sandstones. For each sample, we obtain a single set of microtomographic shadow projections, but run reconstruction twice: one regular high-quality reconstruction, and one using just a quarter of all available shadow projections. Thoughtful manual Indicator Kriging (IK) segmentation of the full-quality image is used as the ground truth for segmentation of images with reduced quality. We assess the generalization capability of CNN by splitting our dataset into training and validation sets by five different manners. In addition, we compare neural networks results with segmentation by IK and thresholding. Segmentation outcomes by 2D and 3D U-nets are comparable to IK, but the deep neural networks operate in automatic mode, and there is big room for improvements in solutions based on CNN. The main difficulties are associated with the segmentation of fine structures that are relatively uncommon in our dataset.

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