CT image-based computer-aided system for orbital prosthesis rehabilitation

2015 ◽  
Vol 53 (10) ◽  
pp. 943-950 ◽  
Author(s):  
Shuang Li ◽  
Caiwen Xiao ◽  
Liuyao Duan ◽  
Chunlong Fang ◽  
Yuanliang Huang ◽  
...  
Keyword(s):  
Ct Image ◽  
Computer Aided ◽  
Orbital Prosthesis

scholarly journals Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Yongfeng Gao ◽  
Jiaxing Tan ◽  
Zhengrong Liang ◽  
Lihong Li ◽  
Yumei Huo
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Pulmonary Nodules ◽  
Ct Image ◽  
Computer Aided Detection ◽  
Ct Scanner ◽  
Human Visual Perception ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Cade System

AbstractComputer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists’ examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.

scholarly journals Computer-Aided Diagnosis (CAD) to Detect Abnormality on CT Image of Liver

2020 ◽  
Vol 1505 ◽  
pp. 012005
Author(s):  
W S Wahyuni ◽  
P Prajitno ◽  
D S Soejoko
Keyword(s):  
Computer Aided Diagnosis ◽  
Ct Image ◽  
Computer Aided ◽  
Aided Diagnosis

COMPUTER-AIDED DIFFERENTIATION OF SOLID RENAL MASS WITH CT IMAGE USING CLASSIFICATION AND REGRESSION TREES MODEL

2008 ◽  
Vol 179 (4S) ◽  
pp. 728-728
Author(s):  
Kazumi Kamoi ◽  
Brian Herts ◽  
Nidhi Sharma ◽  
Andre Berger ◽  
Georges-Pascal Haber ◽  
...  
Keyword(s):  
Renal Mass ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Ct Image ◽  
Computer Aided ◽  
Solid Renal Mass ◽  
Classification And Regression

Computer-Aided CT Image Analysis Based on Clustered Hounsfield Values

2007 ◽  
Vol 51 (1) ◽  
pp. 235 ◽  
Author(s):  
Hye Kyung Kim ◽  
Oyeon Kum ◽  
Nelson L. Max
Keyword(s):  
Image Analysis ◽  
Ct Image ◽  
Computer Aided ◽  
Ct Image Analysis
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