scholarly journals An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images

2018 ◽  
Vol 45 (6) ◽  
pp. 2542-2559 ◽  
Author(s):  
Marco Caballo ◽  
John M. Boone ◽  
Ritse Mann ◽  
Ioannis Sechopoulos
Keyword(s):  
Computed Tomography ◽  
Breast Tissue ◽  
Automatic Segmentation ◽  
Segmentation Algorithm ◽  
Tissue Classification ◽  
Computed Tomography Images ◽  
Breast Tissue Classification ◽  
Breast Computed Tomography

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

Feature space optimization for brain tissue classification in non-contrast computed tomography images

2021 ◽  
Author(s):  
C.U. Sanchez-Guerrero ◽  
N. Gordillo-Castillo ◽  
J.M. Mejia-Munoz ◽  
B. Mederos-Madrazo ◽  
I. Cruz-Aceves
Keyword(s):  
Computed Tomography ◽  
Brain Tissue ◽  
Feature Space ◽  
Tissue Classification ◽  
Computed Tomography Images

scholarly journals Automatic Segmentation of Liver from Abdominal Computed Tomography Images using Energy Feature

2021 ◽  
Vol 67 (1) ◽  
pp. 709-722
Author(s):  
Prabakaran Rajamanickam ◽  
Shiloah Elizabeth Darmanayagam ◽  
Sunil Retmin Raj Cyril Raj
Keyword(s):  
Computed Tomography ◽  
Automatic Segmentation ◽  
Abdominal Computed Tomography ◽  
Computed Tomography Images ◽  
Energy Feature

scholarly journals Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

2021 ◽  
Vol 68 (2) ◽  
pp. 2451-2467
Author(s):  
Javaria Amin ◽  
Muhammad Sharif ◽  
Muhammad Almas Anjum ◽  
Yunyoung Nam ◽  
Seifedine Kadry ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Computed Tomography Images

scholarly journals Automatic Segmentation of the Gross Target Volume in Non-Small Cell Lung Cancer Using a Modified Version of ResNet

2020 ◽  
Vol 19 ◽  
pp. 153303382094748
Author(s):  
Fuli Zhang ◽  
Qiusheng Wang ◽  
Haipeng Li
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Automatic Segmentation ◽  
Target Volume ◽  
Small Cell ◽  
Small Cell Lung ◽  
Computed Tomography Images ◽  
Positive Rate

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.

New classification of dynamic computed tomography images predictive of malignant characteristics of hepatocellular carcinoma

2010 ◽  
Vol 40 (10) ◽  
pp. 1006-1014 ◽  
Author(s):  
Yusuke Kawamura ◽  
Kenji Ikeda ◽  
Miharu Hirakawa ◽  
Hiromi Yatsuji ◽  
Hitomi Sezaki ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Hepatocellular Carcinoma ◽  
Dynamic Computed Tomography ◽  
New Classification ◽  
Computed Tomography Images
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