scholarly journals Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications

2018 ◽  
Vol 13 (11) ◽  
pp. 1697-1706 ◽  
Author(s):  
Naoki Kamiya ◽  
Jing Li ◽  
Masanori Kume ◽  
Hiroshi Fujita ◽  
Dinggang Shen ◽  
...  
Keyword(s):  
Random Forest ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Paraspinal Muscles ◽  
Multi Scale ◽  
Fully Automatic

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

SU-E-J-50: Fully Automatic Segmentation of Brain Tumor in CT Images

2011 ◽  
Vol 38 (6Part8) ◽  
pp. 3453-3453
Author(s):  
M Gao ◽  
D Wei ◽  
S Chen
Keyword(s):  
Brain Tumor ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Fully Automatic

Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

2013 ◽  
Vol 32 (8) ◽  
pp. 1462-1472 ◽  
Author(s):  
C. Lindner ◽  
S. Thiagarajah ◽  
J. Wilkinson ◽  
The Consortium ◽  
G. Wallis ◽  
...  
Keyword(s):  
Random Forest ◽  
Proximal Femur ◽  
Automatic Segmentation ◽  
Random Forest Regression ◽  
Fully Automatic

AUTOMATIC MULTI-SCALE SEGMENTATION OF INTRAHEPATIC VESSEL IN CT IMAGES FOR LIVER SURGERY PLANNING

2013 ◽  
Vol 27 (01) ◽  
pp. 1357001 ◽  
Author(s):  
YI WANG ◽  
BIN FANG ◽  
JINGRUI PI ◽  
LEI WU ◽  
PATRICK S. P. WANG ◽  
...  
Keyword(s):  
Distribution Function ◽  
Blood Vessels ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Hessian Matrix ◽  
Ct Images ◽  
Vascular Pattern ◽  
Low Contrast ◽  
Multi Scale ◽  
Normal Scale

The processing of blood vessels is an indispensable part in complicated surgeries of livers and hearts as the development of medical image technologies, which requires an automatic segmentation system over CT images of organs. However, the vascular pattern of livers in CT images suffers from low contrast to background so that the existing segmentation technologies are not able to extract the blood vessels completely. In the paper, we propose a new algorithm to extract the blood vessels of livers based on the adaptive multi-scale segmentation. First, we prove that the background histogram of normal scale blood vessels obeys the Gaussian distribution in CT images, and obtain the vascular distribution function from the vascular signal segmented from the background with a local optimal threshold. Second, Hessian matrix is employed to enhance the thin blood vessels before the extraction, and a complete and clear segmentation system for blood vessels is constructed by combining the major and thin blood vessels via filtering. Experimental results show the effectiveness of the proposed method, which is able to extract more complete blood vessels for 3D system, and assist the clinical liver surgeries efficiently.

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