Automatic liver segmentation with CT images based on 3D U-net deep learning approach

2019 ◽  
Author(s):  
Ting Yu Su ◽  
Wei-Tse Yang ◽  
Tsu-Chi Cheng ◽  
Yi-Fei He ◽  
Yu-Hua Fang
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Learning Approach ◽  
Liver Segmentation

Severity and Progression Quantification of COVID-19 in CT Images: a new Deep-Learning Approach

2021 ◽  
Author(s):  
Faridoddin Shariaty ◽  
Vitalii Pavlov ◽  
Elena Velichko ◽  
Tatiana Pervunina ◽  
Mahdi Orooji
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Learning Approach

scholarly journals A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images

2020 ◽  
Vol 30 (12) ◽  
pp. 6517-6527 ◽  
Author(s):  
Qianqian Ni ◽  
Zhi Yuan Sun ◽  
Li Qi ◽  
Wen Chen ◽  
Yi Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Chest Ct ◽  
Learning Approach

scholarly journals MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach

2019 ◽  
Vol 136 ◽  
pp. 56-63 ◽  
Author(s):  
Samaneh Kazemifar ◽  
Sarah McGuire ◽  
Robert Timmerman ◽  
Zabi Wardak ◽  
Dan Nguyen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Learning Approach ◽  
Brain Radiotherapy ◽  
Dosimetric Accuracy ◽  
Synthetic Ct

Multi-class Segmentation of Organ at Risk from Abdominal CT Images: A Deep Learning Approach

2021 ◽  
pp. 425-434
Author(s):  
Muhammad Ibrahim Khalil ◽  
Mamoona Humayun ◽  
N. Z. Jhanjhi ◽  
M. N. Talib ◽  
Thamer A. Tabbakh
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Ct Images ◽  
Learning Approach ◽  
Abdominal Ct ◽  
Organ At Risk ◽  
Class Segmentation

scholarly journals FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images

2019 ◽  
Vol 38 (1) ◽  
pp. 156-166 ◽  
Author(s):  
Sarah E. Gerard ◽  
Taylor J. Patton ◽  
Gary E. Christensen ◽  
John E. Bayouth ◽  
Joseph M. Reinhardt
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Learning Approach

An Automated Method for Detecting the Scar Tissue in the Left Ventricular Endocardial Wall Using Deep Learning Approach

2020 ◽  
Vol 16 (3) ◽  
pp. 206-213
Author(s):  
Yashbir Singh ◽  
Deepa Shakyawar ◽  
Weichih Hu
Keyword(s):  
Deep Learning ◽  
Cardiac Ct ◽  
Scar Tissue ◽  
Ct Images ◽  
Left Ventricular ◽  
Learning Approach ◽  
Morphological Operations ◽  
Image Evaluation ◽  
Automated Method ◽  
Pixel Value

Background: Image evaluation of scar tissue plays a significant role in the diagnosis of cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can be applied to identify possible scar tissue in the left ventricular endocardial wall. Objective: To develop an automated method for detecting the endocardial scar tissue in the left ventricular using Deep learning approach. Method: Pixel values of the endocardial wall for each image in the sequence were extracted. Morphological operations, including defining regions of the endocardial wall of the LV where scar tissue could predominate, were performed. Convolutional Neural Networks (CNN) is a deep learning application, which allowed choosing appropriate features from delayed enhancement cardiac CT images to distinguish between endocardial scar and healthy tissues of the LV by applying pixel value-based concepts. Result: We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity in the detection of endocardial scars using the CNN-based method. Conclusion: Our findings reveal that the CNN-based method yielded robust accuracies in LV endocardial scar detection, which is currently the most extensively used pixel-based method of deep learning. This study provides a new direction for the assessment of scar tissue in imaging modalities and provides a potential avenue for clinical adaptations of these algorithms. Additionally this methodology, in comparison with those in the literature, provides specific advantages in its translational ability to clinical use.

scholarly journals Esophagus segmentation from planning CT images using an atlas-based deep learning approach

2020 ◽  
Vol 197 ◽  
pp. 105685
Author(s):  
João Otávio Bandeira Diniz ◽  
Jonnison Lima Ferreira ◽  
Pedro Henrique Bandeira Diniz ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Learning Approach ◽  
Planning Ct
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