scholarly journals Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xixi Guo ◽  
Yuze Li ◽  
Chunjie Yang ◽  
Yanjiang Hu ◽  
Yun Zhou ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Deep Learning ◽  
Radiofrequency Ablation ◽  
Survival Rate ◽  
Lung Nodule ◽  
Segmentation Algorithm ◽  
Lung Nodules ◽  
Ct Image ◽  
Image Segmentation Algorithm

This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) ( P < 0.05 ); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) ( P < 0.05 ) that was slightly higher for solid and subsolid nodules with a diameter of 5–10 mm (37 cases vs. 28 cases) ( P < 0.05 ). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) ( P < 0.05 ). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.

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 Intelligent Algorithm-Based Computed Tomography Image Features in Diagnosis of the Effect of Radiofrequency Ablation in Liver Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yufeng Cha ◽  
Zhili Wei ◽  
Chi Ma ◽  
Lei Zhang
Keyword(s):  
Computed Tomography ◽  
Radiofrequency Ablation ◽  
Ct Images ◽  
Segmentation Algorithm ◽  
Control Group ◽  
Observation Group ◽  
Ct Image ◽  
Comprehensive Diagnosis ◽  
Kappa Value ◽  
The Difference

To provide a reference for finding a reasonable evaluation method for treatment effect of radiofrequency ablation (RFA), computed tomography (CT) image optimized by the intelligent segmentation algorithm was utilized to evaluate the liver condition of hepatocellular carcinoma (HCC) patients after RFA and to estimate the patient’s prognosis. Eighty-eight patients with HCC who needed RFA surgery after diagnosis in our hospital were selected. The CT images before optimization were set as the control group; the CT images after optimization were set as the observation group. Comprehensive diagnosis was taken as the gold standard to compare the ablation range and residual lesions under CT scans before and after surgery. The results showed that the consistency of the two sets of CT images was compared with comprehensive diagnosis under different diameters of the lesion. The difference between the two groups was not statistically considerable when the diameter of the lesion was less than 50 mm ( P > 0.05 ). For lesions larger than 50 mm in diameter, the consistency of the observation group (83%) was remarkably higher than that of the control group (40%), and the difference was substantial ( P < 0.05 ). The kappa value of the observation group was 0.84 and that of the control group was 0.78. The kappa value of observation group was better than the control group, with considerable difference ( P < 0.05 ). In conclusion, the diagnostic effect of CT image based on intelligent segmentation algorithm was superior to conventional diagnosis when the diameter of the lesion was larger than 50 mm. Moreover, the overall improvement rate of patients after RFA treatment was far greater than the recurrence rate, indicating that the clinical adoption of RFA was very meaningful.

scholarly journals An Improved Image Segmentation Algorithm CT Superpixel Grid Using Active Contour

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuntao Wei ◽  
Xiaojuan Wang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Segmentation Algorithm ◽  
Initial Contour ◽  
Ct Image ◽  
Image Edge ◽  
Growth Graph ◽  
Region Growth ◽  
Image Segmentation Algorithm

The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford-Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.

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