Liver cancer detection by using transition features obtained from multi-phase CT images

2005 ◽  
Author(s):  
Shigeto Watanabe ◽  
Yoshito Mekada ◽  
Junichi Hasegawa ◽  
Junichiro Toriwaki
Keyword(s):  
Liver Cancer ◽  
Cancer Detection ◽  
Ct Images ◽  
Multi Phase

Feature based CT image registration of liver cancer

2021 ◽  
pp. 095441192110402
Author(s):  
Abhay Krishan ◽  
Deepti Mittal
Keyword(s):  
Image Registration ◽  
Liver Cancer ◽  
Ct Images ◽  
High Rate ◽  
Diagnostic Systems ◽  
Large Tumor ◽  
Moving Images ◽  
Feature Based ◽  
Multi Phase ◽  
Rate Of Success

Computer-aided diagnostic systems (CADS) assist radiologists in classifying liver cancer using computed tomography (CT) images. To enhance diagnosis performance, image sequences are recorded at various time points in a single/multi-view format. Mutual information (MI) is a widely used medical image registration metric with a high rate of success, but it can result in misregistration due to a lack of spatial details. To address this issue and to establish anatomical correspondence between multi-phase CT images of the liver, a features-based technique is developed in this article. The proposed model uses fixed and moving images as inputs, with both images having the same dimensions. The registered images are the two images that differ in terms of their combinations/colors. In the output registered images, the tumor in the liver portion has classes with viewpoints. There is an appropriate way to view the tumor, and the output registered images should permit concluding that the registered image of the delayed phases, with a longer delay time, contains the most region portion within the output registered image. The detected and matched values are greater than the values of the feature outcomes. Having a large tumor provides valuable information in the presenting form for discussing the variation of the various phases and delayed testing results. And this will aid the radiologist in making an accurate diagnosis.

scholarly journals Analysis of the advantage of individual PTVs defined on axial 3D CT and 4D CT images for liver cancer

2012 ◽  
Vol 13 (6) ◽  
pp. 62-71 ◽  
Author(s):  
Fengxiang Li ◽  
Jianbin Li ◽  
Jun Xing ◽  
Yingjie Zhang ◽  
Tingyong Fan ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Ct Images ◽  
3D Ct ◽  
4D Ct

Improvement of Liver Cancer Detection with Simultaneous Assessment of Circulating Levels of Free Alpha-Fetoprotein (AFP) and Afp-Igm Complexes

2004 ◽  
Vol 19 (2) ◽  
pp. 155-159 ◽  
Author(s):  
L. Beneduce ◽  
F. Castaldi ◽  
M. Marino ◽  
N. Tono ◽  
A. Gatta ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Liver Cancer ◽  
Cancer Detection ◽  
Alpha Fetoprotein ◽  
Standard Curve ◽  
Cirrhotic Patients ◽  
The Mean ◽  
Automated Immunoassay ◽  
Circulating Levels ◽  
Simultaneous Assessment

We assessed the presence of alpha-fetoprotein (AFP) complexed with IgM (AFP-IgM IC) in serum of patients affected by hepatocellular carcinoma (HCC), cirrhosis and chronic hepatitis as well as in healthy subjects by means of a dedicated ELISA assay. The amount of AFP-IgM IC was expressed in arbitrary units (AU) on a reference standard curve. Free AFP (FAFP) levels were determined in parallel in each sample by means of an automated immunoassay system. The mean serum concentration of AFP-IgM IC was significantly higher in HCC patients (mean ± SD: 1378.3 ± 2935.7 AU/mL) than in cirrhotic patients (129.8 ± 261.4 AU/mL) and in patients with chronic hepatitis (80.9 ± 168.9 AU/mL) (p<0.01). HCC patients had FAFP values above the 20 ng/mL cutoff in 44% of cases (22/50) and AFP-IgM IC values above the 120 AU/mL cutoff in 60% of cases (30/50). The occurrence of the free and IgM-complexed form of circulating AFP did not overlap, and 82% of patients (41/50) were positive for at least one marker. The results indicate that AFP-IgM IC is a complementary serological marker to FAFP and that the combination of these biomarkers may be useful in the diagnosis of liver cancer.

scholarly journals Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
He Sui ◽  
Ruhang Ma ◽  
Lin Liu ◽  
Yaozong Gao ◽  
Wenhai Zhang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Ct Images ◽  
Chest Ct ◽  
Validation Dataset ◽  
Esophageal Wall ◽  
Detection Model ◽  
Model Training ◽  
Sensitivity Specificity

ObjectiveTo develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images.MethodsWe retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer. The CNN is a VB-Net segmentation network that segments the esophagus and automatically quantifies the thickness of the esophageal wall and detect positions of esophageal lesions. To validate this model, 52 false negatives and 48 normal cases were collected further as the second dataset. The average performance of three radiologists and that of the same radiologists aided by the model were compared.ResultsThe sensitivity and specificity of the esophageal cancer detection model were 88.8% and 90.9%, respectively, for the validation dataset set. Of the 52 missed esophageal cancer cases and the 48 normal cases, the sensitivity, specificity, and accuracy of the deep learning esophageal cancer detection model were 69%, 61%, and 65%, respectively. The independent results of the radiologists had a sensitivity of 25%, 31%, and 27%; specificity of 78%, 75%, and 75%; and accuracy of 53%, 54%, and 53%. With the aid of the model, the results of the radiologists were improved to a sensitivity of 77%, 81%, and 75%; specificity of 75%, 74%, and 74%; and accuracy of 76%, 77%, and 75%, respectively.ConclusionsDeep learning-based model can effectively detect esophageal cancer in unenhanced chest CT scans to improve the incidental detection of esophageal cancer.

Sign in / Sign up

Export Citation Format

Share Document