scholarly journals Application of CT Image Technology Based on Nearest Neighbor Propagation Clustering Segmentation Algorithm in Lung Cancer Radiotherapy

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Weixiang Chen ◽  
Jianfu Zhao ◽  
Zhenhui Dai ◽  
Mingyue Lv ◽  
Zhenhua Yang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Segmentation ◽  
Nearest Neighbor ◽  
Automatic Segmentation ◽  
Segmentation Algorithm ◽  
Target Area ◽  
Ct Image ◽  
Tumor Target ◽  
Cancer Radiotherapy ◽  
Pet Ct

Objective. This paper uses the nearest neighbor propagation clustering segmentation algorithm to explore the impact of PET/CT image segmentation technology on lung cancer radiotherapy planning. Methods. In this paper, PET/CT scan was performed on 12 patients with nonmetastatic lung cancer. The self-written automatic segmentation program based on PCNN model is used to segment the PET target area, and then the tumor target area is manually sketched based on CT images and PET/CT images, and the intensity-modulated radiotherapy plan is formulated with the same parameters. Target volume and dose distribution were analyzed. Results. There was no statistical difference between the PET automatic segmentation target area and the PET manual contouring target area ( P < 0.05 ); the segmentation method was accurate and reliable; the difference between the CT manual contouring target area was statistically significant ( P 0.05 ). Conclusion. Based on the nearest neighbor propagation clustering segmentation algorithm, PET/CT image segmentation technology improves the accuracy of tumor target area delineation. The radiotherapy plan based on the segmentation target area can reduce the normal tissue exposure range and reduce the incidence of complications.

Enhancing 18F-FDG-PET/CT analysis in lung cancer patients

2015 ◽  
Vol 54 (06) ◽  
pp. 247-254 ◽  
Author(s):  
A. Kapfhammer ◽  
T. Winkens ◽  
T. Lesser ◽  
A. Reissig ◽  
M. Steinert ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
Chest Wall ◽  
Fdg Pet ◽  
Ct Image ◽  
Lung Cancers ◽  
Pet Ct ◽  
Peripheral Lung ◽  
Fdg Pet Ct ◽  
Tumour Infiltration

SummaryAim: To retrospectively evaluate the feasibility and value of CT-CT image fusion to assess the shift of peripheral lung cancers with/-out chest wall infiltration, comparing computed tomography acquisitions in shallow-breathing (SB-CT) and deep-inspiration breath-hold (DIBH-CT) in patients undergoing FDG-PET/ CT for lung cancer staging. Methods: Image fusion of SB-CT and DIBH-CT was performed with a multimodal workstation used for nuclear medicine fusion imaging. The distance of intrathoracic landmarks and the positional shift of tumours were measured using semitransparent overlay of both CT series. Statistical analyses were adjusted for confounders of tumour infiltration. Cutoff levels were calculated for prediction of no-/infiltration. Results: Lateral pleural recessus and diaphragm showed the largest respiratory excursions. Infiltrating lung cancers showed more limited respiratory shifts than non-infiltrating tumours. A large respiratory tumour-motility accurately predicted non-infiltration. However, the tumour shifts were limited and variable, limiting the accuracy of prediction. Conclusion: This pilot fusion study proved feasible and allowed a simple analysis of the respiratory shifts of peripheral lung tumours using CT-CT image fusion in a PET/CT setting. The calculated cutoffs were useful in predicting the exclusion of chest wall infiltration but did not accurately predict tumour infiltration. This method can provide additional qualitative information in patients with lung cancers with contact to the chest wall but unclear CT evidence of infiltration undergoing PET/CT without the need of additional investigations. Considering the small sample size investigated, further studies are necessary to verify the obtained results.

A Novel Adaptive PET/CT Image Fusion Algorithm

2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
High Frequency ◽  
Malignant Tumors ◽  
Low Frequency ◽  
Combination Method ◽  
Ct Image ◽  
Fusion Algorithm ◽  
Pet Ct ◽  
Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

scholarly journals A Comparative Study of Auto-contouring Softwares in Delineation of Organs at Risk in Lung Cancer and Rectal Cancer

2021 ◽  
Author(s):  
weijun chen ◽  
Cheng Wang ◽  
Wenming Zhan ◽  
Yongshi Jia ◽  
Fangfang Ruan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
At Risk ◽  
Rectal Cancer ◽  
Organs At Risk ◽  
Similarity Index ◽  
Slight Modification ◽  
Ct Images ◽  
Target Area ◽  
Ct Image ◽  
Significant Difference

Abstract Background:Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious.This study aims to evaluate the results of two automatic contouring software on OAR definition of CT images of lung cancer and rectal cancer patients. Methods: The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were outlined by the same experienced doctor as references, and then the same datasets were automatically contoured based on AiContour®© (Manufactured by Linking MED, China) and Raystation®© (Manufactured by Raysearch, Sweden) respectively. Overlap index (OI), Dice similarity index (DSC) and Volume difference (DV) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. Results: The results of AiContour®© on OI and DSC were better than that of Raystation®© with statistical difference. There was no significant difference in DV between the results of two software. Conclusions: With AiContour®©, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Raystation®©, auto-contouring results in most OAR is not as good as AiContour®©, and only the auto-contouring results of some organs can be used clinically after modification.

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information

2012 ◽  
Vol 36 (1) ◽  
pp. 47-53 ◽  
Author(s):  
Yong Xia ◽  
Stefan Eberl ◽  
Lingfeng Wen ◽  
Michael Fulham ◽  
David Dagan Feng
Keyword(s):  
Image Segmentation ◽  
Ct Image ◽  
Dual Modality ◽  
Adaptive Use ◽  
Brain Pet ◽  
Pet Ct ◽  
Anatomical Information

scholarly journals Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III

2016 ◽  
Vol 43 (8) ◽  
pp. 1477-1485 ◽  
Author(s):  
Marie-Charlotte Desseroit ◽  
Dimitris Visvikis ◽  
Florent Tixier ◽  
Mohamed Majdoub ◽  
Rémy Perdrisot ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Features ◽  
Small Cell ◽  
Clinical Staging ◽  
Cancer Stage ◽  
Ct Image ◽  
Small Cell Lung ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Expectant Vomiting of Lung Cancer Patients Using Positron Emission Tomography-Computed Tomography Image Analysis and Continuous Nursing Theory

2020 ◽  
Vol 10 (9) ◽  
pp. 2236-2241
Author(s):  
Chengyi Zhou ◽  
Junru Wang ◽  
Min Tao ◽  
Zhiqin Zhou ◽  
Wei Yang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Health Knowledge ◽  
Standard Uptake Value ◽  
Nausea And Vomiting ◽  
Control Group ◽  
Ct Image ◽  
Pet Ct ◽  
Significant Difference ◽  
Negative Coping ◽  
Experimental Group

Objective: The objective is to study the effect of continuous nursing on the vomiting of patients with expectant vomiting of lung cancer, and to establish a three-dimensional segmentation model of PET-CT image, so as to provide an effective nursing intervention for patients with expectant vomiting of lung cancer. Methods: In this study, the sampling method is adopted. We collected 68 patients (over 18 years old) diagnosed with lung cancer from May 2016 to June 2018 as the study subjects. Patients are divided into experimental group and control group. Before discharge, the patients in the control group are given general discharge guidance and health knowledge guidance. The patients in the experimental group are treated with continuous nursing until the next admission, except for general discharge guidance and health knowledge guidance. The cycle is a period of intermittent chemotherapy. According to the general data questionnaire designed by the researchers themselves, the criteria of acute and subacute toxicity of anticancer drugs developed by the World Health Organization (WHO), and the simple coping style questionnaire, the data are collected. SPSS 22.0 is used for analysis. The rank sum test is used in the grading of expected nausea and vomiting. The score of self coping ability is compared within the group by paired sample t-test, and P < 0.05 is statistically significant. Results: First, before continuous nursing, there is no significant difference in the expected nausea and vomiting between the two groups (P = 0.299). After continuous nursing, in the experimental group, nausea and vomiting is significantly improved (P < 0.001). Second, the positive and negative coping scores of the two groups are 15.98±1.11 and 16.99±1.23, respectively. There is no significant difference between the two groups (P > 0.05). After continuous nursing, the experimental group is compared with the control group. There is a significant difference between positive coping score (19.21±2.12) and negative coping score (16.27±1.53) (P < 0.01). Thirdly, the pixels with the standard uptake value (SUV) > 1/4 of the maximum standard uptake value (SUVmax) are selected as the basic tumor range, which can accurately predict the tumor size and range. Conclusion: PET-CT image analysis and continuous nursing can reduce the degree of nausea and vomiting in patients with lung cancer expectant vomiting, predict the size of lung cancer tumor, improve the patients’ self-response ability and the cure rate of tumor, which is worth promoting in patients with lung cancer expectant vomiting.

Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer

2012 ◽  
Vol 102 (2) ◽  
pp. 239-245 ◽  
Author(s):  
Manushka Vaidya ◽  
Kimberly M. Creach ◽  
Jennifer Frye ◽  
Farrokh Dehdashti ◽  
Jeffrey D. Bradley ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tumor Response ◽  
Ct Image ◽  
Image Characteristics ◽  
Pet Ct

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.

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