scholarly journals Integrated lung field segmentation of injured region with anatomical structure analysis by failure–recovery algorithm from chest CT images

2014 ◽  
Vol 12 ◽  
pp. 28-38 ◽  
Author(s):  
Yuma Iwao ◽  
Toshiyuki Gotoh ◽  
Seiichiro Kagei ◽  
Tae Iwasawa ◽  
Marcos de Sales Guerra Tsuzuki
Keyword(s):  
Structure Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Anatomical Structure ◽  
Failure Recovery ◽  
Lung Field ◽  
Recovery Algorithm ◽  
Injured Region ◽  
Lung Field Segmentation

Integrated Lung Field Segmentation of Injured Regions and Anatomical Structures from Chest CT Images

2012 ◽  
Vol 45 (18) ◽  
pp. 85-90 ◽  
Author(s):  
Yuma Iwao ◽  
Toshiyuki Gotoh ◽  
Seiichiro Kagei ◽  
Tae Iwasawa ◽  
Marcos de Sales Guerra Tsuzuki
Keyword(s):  
Ct Images ◽  
Chest Ct ◽  
Lung Field ◽  
Anatomical Structures ◽  
Lung Field Segmentation

Automated lung field segmentation in CT images using mean shift clustering and geometrical features

2013 ◽  
Author(s):  
Chanukya Krishna Chama ◽  
Sudipta Mukhopadhyay ◽  
Prabir Kumar Biswas ◽  
Ashis Kumar Dhara ◽  
Mahendra Kasuvinahally Madaiah ◽  
...  
Keyword(s):  
Mean Shift ◽  
Ct Images ◽  
Lung Field ◽  
Mean Shift Clustering ◽  
Geometrical Features ◽  
Lung Field Segmentation

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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