scholarly journals Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image

2021 ◽  
pp. 104667
Author(s):  
Hyung Min Kim ◽  
Taehoon Ko ◽  
In Young Choi ◽  
Jun-Pyo Myong
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Learning Model ◽  
Segmentation Method ◽  
Lung Segmentation ◽  
Tomography Image ◽  
Diagnosis Algorithm ◽  
Computed Tomography Image ◽  
Deep Learning Model

scholarly journals Multi-component segmentation of X-ray computed tomography (CT) image using multi-Otsu thresholding algorithm and scanning electron microscopy

2017 ◽  
Vol 35 (3) ◽  
pp. 281-294 ◽  
Author(s):  
Pengfei Zhang ◽  
Shuangfang Lu ◽  
Junqian Li ◽  
Ping Zhang ◽  
Liujuan Xie ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Computed Tomography ◽  
Scanning Electron Microscopy ◽  
Segmentation Method ◽  
X Ray ◽  
Otsu Thresholding ◽  
Tomography Image ◽  
Thresholding Algorithm ◽  
Computed Tomography Image ◽  
Scanning Electron

X-ray computed tomography is an efficient method for quantitatively estimating the characteristics and heterogeneity of shales in three dimensions. A threshold is commonly used to separate pore-fractures from the background image. However, few studies have focused on the multi-component segmentation of computed tomography images. To obtain the distribution characteristics of different components in three dimensions, a segmentation method was proposed that combines a multi-Otsu thresholding algorithm with scanning electron microscopy. The gray value distributions of different components were first determined using this method. Then, the shale components were divided into several groups based on these gray values. The threshold of each component group was determined using the multi-Otsu thresholding algorithm. The computed tomography image stacks of two shale samples were processed using this segmentation method, and another computed tomography image stack was used to verify the method. The results showed that (1) the multi-component segmentation method can successfully segment computed tomography image stacks using the calculated values determined by computed tomography, which agree well with the measured values obtained from X-ray diffraction, total organic carbon, and porosity tests in the laboratory; (2) samples with similar provenances and mineral compositions have the same gray value distribution in the back scattering scanning electron microscopy and computed tomography images; (3) this method is superior in both the effectiveness and efficiency of the computed tomography image stack segmentation of samples according to the gray value distribution, as determined by samples with similar provenances and mineral compositions.

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning

2021 ◽  
Vol 66 (6) ◽  
pp. 065029
Author(s):  
Yusuke Nomura ◽  
Sodai Tanaka ◽  
Jeff Wang ◽  
Hiroki Shirato ◽  
Shinichi Shimizu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Uncertainty Estimation ◽  
Image Correction ◽  
Proton Computed Tomography ◽  
Tomography Image ◽  
Computed Tomography Image

Automated Lung Segmentation on Computed Tomography Image for the Diagnosis of Lung Cancer.

2018 ◽  
Vol 22 (3) ◽  
Author(s):  
Ratishchandra Huidrom ◽  
Yambem Jina Chanu ◽  
Khumanthem Manglem Singh
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Lung Segmentation ◽  
Tomography Image ◽  
Diagnosis Of Lung Cancer ◽  
Computed Tomography Image
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