scholarly journals Computed Tomography Image Based on Intelligent Segmentation Algorithm in the Diagnosis of Ovarian Tumor

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ling Zhu ◽  
Yucheng He ◽  
Nan He ◽  
Lanhua Xiao
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Comprehensive Evaluation ◽  
Clinical Analysis ◽  
Ovarian Tumors ◽  
Ct Image ◽  
Density Peak ◽  
Benign Ovarian Tumors ◽  
Computed Tomography Image

This study was to explore the application of computed tomography (CT) images based on intelligent segmentation algorithms in the analysis of ovarian tumors, so as to provide a theoretical basis for clinical diagnosis of ovarian tumors. In this study, 100 patients with ovarian tumors were selected as the research objects and performed CT imaging examinations; a convolutional neural networks (CNN) algorithm model was constructed and applied to CT diagnostic image segmentation of patients with ovarian tumors, so as to analyze the effectiveness of the proposed algorithm for CT image segmentation. As a result, the image was segmented three times under the CNN algorithm, and the numbers of true positives (TP) were 50, 49, and 50, respectively; the numbers of false positives (FP) were 1, 2, and 1, respectively; the numbers of false negatives (FN) were 2, 3, and 2, respectively; and the numbers of true negatives (TN) were 47, 46, and 47, respectively. Thus, there was no great difference in the three measured values P ≥ 0.05 . The accuracy of the CNN algorithm was 0.97, 0.95, and 0.97, respectively, for the three times of segmentation; the precision was 0.98, 0.96, and 0.98, respectively; the recall was 0.96, 0.94, and 0.96, respectively. Thus, the accuracy, precision, and recall of the three measurements were not greatly different P ≥ 0.05 . In addition, the F1 values of three measurements were 0.97, 0.94, and 0.97, respectively, which all were close to 1, showing no statistically great difference P ≥ 0.05 . The segmentation accuracy, precision, and recall of the algorithm in this study were greatly greater than the SE-Res Block U-shaped CNN algorithm, and the density peak clustering algorithm, and the differences were statistically significant P < 0.05 . In short, the CNN algorithm showed high accuracy, precision, recall, and comprehensive evaluation values for CT image segmentation, which made the diagnosis of malignant or benign ovarian tumors more effective and provided reliable theoretical guidance for clinical analysis of ovarian tumors.

Optimal image segmentation protocol for 3D printing of aortic dissection through open-source software

2021 ◽  
Author(s):  
Chia-An Wu ◽  
Andrew Squelch ◽  
Zhonghua Sun
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
3D Printing ◽  
Aortic Dissection ◽  
Open Source ◽  
Open Source Software ◽  
Type B ◽  
Manual Intervention ◽  
Computed Tomography Images ◽  
Computed Tomography Image

Aim: To determine the optimal image segmentation protocol that minimizes the amount of manual intervention and correction required while extracting 3D model geometries suitable for 3D printing of aortic dissection (AD) using open-source software. Materials & methods: Computed tomography images of two type B AD cases were selected with images segmented using a 3D Slicer to create a hollow model containing the aortic wall and intimal tear. A workflow composed of filters, lumen extraction and outer surface creation was developed. Results & conclusion: The average difference in measurements at 14 anatomical locations between the Standard Tessellation Language file and the computed tomography image for cases 1 and 2 were 0.29 and 0.32 mm, respectively. The workflow for the image segmentation of type B AD was able to produce a high-accuracy 3D-printed model in a short time through open-source software.

scholarly journals Analysis of the Clinical Characteristics of Tuberculosis Patients based on Multi-Constrained Computed Tomography (CT) Image Segmentation Algorithm

2021 ◽  
Vol 37 (6-WIT) ◽  
Author(s):  
Feng Zhu ◽  
Bo Zhang
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Image Segmentation ◽  
Pulmonary Tuberculosis ◽  
Clinical Characteristics ◽  
Ct Image ◽  
Segmentation Method ◽  
Tuberculosis Patients ◽  
Cancer Group ◽  
Image Segmentation Algorithm

Objective: We used U-shaped convolutional neural network (U_Net) multi-constraint image segmentation method to compare the diagnosis and imaging characteristics of tuberculosis and tuberculosis with lung cancer patients with Computed Tomography (CT). Methods: We selected 160 patients with tuberculosis from the severity scoring (SVR) task is provided by Image CLEF Tuberculosis 2019. According to the type of diagnosed disease, they were divided into tuberculosis combined with lung cancer group and others group, all patients were given chest CT scan, and the clinical manifestations, CT characteristics, and initial suspected diagnosis and missed diagnosis of different tumor diameters were observed and compared between the two groups. Results: There were more patients with hemoptysis and hoarseness in pulmonary tuberculosis combined with lung cancer group than in the pulmonary others group (P<0.05), and the other symptoms were not significantly different (P>0.05). Tuberculosis combined with lung cancer group had fewer signs of calcification, streak shadow, speckle shadow, and cavitation than others group; however, tuberculosis combined with lung cancer group had more patients with mass shadow, lobular sign, spines sign, burr sign and vacuole sign than others group. Conclusion: The symptoms of hemoptysis and hoarseness in pulmonary tuberculosis patients need to consider whether the disease has progressed and the possibility of lung cancer lesions. CT imaging of pulmonary tuberculosis patients with lung cancer usually shows mass shadows, lobular signs, spines signs, burr signs, and vacuoles signs. It can be used as the basis for its diagnosis. Simultaneously, the U-Net-based segmentation method can effectively segment the lung parenchymal region, and the algorithm is better than traditional algorithms. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 How to cite this:Zhu F, Zhang B. Analysis of the Clinical Characteristics of Tuberculosis Patients based on Multi-Constrained Computed Tomography (CT) Image Segmentation Algorithm. Pak J Med Sci. 2021;37(6):1705-1709. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Value of multidetector computed tomography image segmentation for preoperative planning in general surgery

2011 ◽  
Vol 26 (3) ◽  
pp. 616-626 ◽  
Author(s):  
Vincenzo Ferrari ◽  
Marina Carbone ◽  
Carla Cappelli ◽  
Luigi Boni ◽  
Franca Melfi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Multidetector Computed Tomography ◽  
General Surgery ◽  
Preoperative Planning ◽  
Tomography Image ◽  
Computed Tomography Image

An Efficient Algorithm Optimization of CT Images Segmentation Based on K-Means Clustering

2014 ◽  
Vol 530-531 ◽  
pp. 386-389 ◽  
Author(s):  
Guo Qiang Ma ◽  
Xiao Juan Wang
Keyword(s):  
Image Segmentation ◽  
High Performance ◽  
Clustering Algorithm ◽  
Computation Time ◽  
Ct Images ◽  
Ct Image ◽  
Algorithm Optimization ◽  
Tomography Image ◽  
Segmentation Algorithms ◽  
The Difference

Computer tomography image (CT Image) segmentation algorithms have a number of advantages. However, most of these image segmentation algorithms suffer from long computation time because the number of pixels and the encoding parameters is large. We optimized the k-means clustering program with MATLAB language in order to improve the efficiency and stability of k-clustering algorithm in CT image segmentation. One hundred CT images are used to test the proposed method code and compare with the k-means function of the MATLAB R2012a Statistics Toolbox. We analyzed the difference of the two kinds program running time using single factor analysis of variance (ANOVA) and observed the efficiency and robustness of the segmentation results. The experimental results show that the optimized k-means clustering algorithm code has higher efficiency and robustness of segmentation. High performance of the proposed k-means clustering program is illustrated in terms of both the evaluation performance and computation time, compared with some current segmentation methods. It is empirically shown that the proposed k-means clustering program is robust and efficient for CT images segmentation.

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