Non-Rigid Liver Registration in Liver Computed Tomography Images Using Elastic Method with Global and Local Deformations

The study of follow-up liver computed tomography (CT) images is required for the early diagnosis and treatment evaluation of liver cancer. Although this requirement has been manually performed by doctors, the demands on computer-aided diagnosis are dramatically growing according to the increased amount of medical image data by the recent development of CT. However, conventional image segmentation, registration, and skeletonization methods cannot be directly applied to clinical data due to the characteristics of liver CT images varying largely by patients and contrast agents. In this paper, we propose non-rigid liver segmentation using elastic method with global and local deformation for follow-up liver CT images. To manage intensity differences between two scans, we extract the liver vessel and parenchyma in each scan. And our method binarizes the segmented liver parenchyma and vessel, and performs the registration to minimize the intensity difference between these binarized images of follow-up CT images. The global movements between follow-up CT images are corrected by rigid registration based on liver surface. The local deformations between follow-up CT images are modeled by non-rigid registration, which aligns images using non-rigid transformation, based on locally deformable model. Our method can model the global and local deformation between follow-up liver CT scans by considering the deformation of both the liver surface and vessel. In experimental results using twenty clinical datasets, our method matches the liver effectively between follow-up portal phase CT images, enabling the accurate assessment of the volume change of the liver cancer. The proposed registration method can be applied to the follow-up study of various organ diseases, including cardiovascular diseases and lung cancer.

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An efficient feature-based non-rigid registration of multiphase liver CT images using matching region partition

2010 5th IEEE Conference on Industrial Electronics and Applications ◽

10.1109/iciea.2010.5515185 ◽

2010 ◽

Author(s):

Ying Cao ◽

Hui-yan Jiang ◽

Ting-jun Qian ◽

Zhi-liang Zhu ◽

Bei-lei Wang ◽

...

Keyword(s):

Ct Images ◽

Rigid Registration ◽

Liver Ct ◽

Region Partition ◽

Feature Based

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Computed Tomography Image Feature under Intelligent Algorithms in Diagnosing the Effect of Humanized Nursing on Neuroendocrine Hormones in Patients with Primary Liver Cancer

Journal of Healthcare Engineering ◽

10.1155/2021/4563100 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xiujie Wang ◽

Lin Liu ◽

Na Ma ◽

Xinxin Zhao

Keyword(s):

Computed Tomography ◽

Liver Cancer ◽

Primary Liver Cancer ◽

Ct Images ◽

Image Feature ◽

Control Group ◽

Intelligent Algorithms ◽

Residual Coding ◽

Computed Tomography Image ◽

Experimental Group

This study was to explore the application value of computed tomography (CT) images processed by intelligent algorithm denoising in the evaluation of humanized nursing in postoperative neuroendocrine hormone changes in patients with primary liver cancer (PLC). In this study, a simple-structured recursive residual coding and decoding (RRCD) algorithm was constructed on the basis of residual network, which can effectively remove artifacts and noise in CT images and can also restore image details and lesion features well. In addition, 60 postoperative patients with primary liver cancer were collected and divided into routine nursing control group (30 cases) and humanized nursing experimental group (30 cases). After a period of nursing, CT images based on intelligent algorithms were evaluated by determining the hormone content. The results showed that the focal necrosis rate (FNR) of the experimental group was 6%. The adrenocorticotropic hormone (ACTH) levels of 6 and 15 days after admission (T3 and T4) were 41.25 ± 3.81 pg/mL and 19.55 ± 1.72 pg/mL, respectively. The cortisol levels of days 6, 15, and 30 after admission (T3, T4, and T5) were 424.86 ± 16.82 nmol/L, 277.98 ± 14.36 nmol/L, and 241.53 ± 13.27 nmol/L, respectively. Estradiol levels were 53.48 ± 11.19 pg/mL, 41.64 ± 9.28 pg/mL, and 30.59 ± 8.16 pg/mL, respectively. Testosterone levels were 2.18 ± 1.14 ng/mL, 1.78 ± 1.03 ng/mL, and 1.42 ± 0.69 ng/mL, respectively. Self-Rating Anxiety Scale (SAS) scores were 40.24 ± 5.81 points, 36.55 ± 5.02 points, and 32.53 ± 4.8 points, respectively. There were 24 cases, 27 cases, 23 cases, and 21 patients who followed no smoking and drinking, taking medication on time, diet control, and self-monitoring. The scores of physical function, self-cognition, emotional function, and social function were 62.59 ± 6.82 points, 69.26 ± 8.14 points, 73.89 ± 6.35 points, and 66.88 ± 7.04 points, which were better than those of the control group in all aspects ( P < 0.05 ). In short, the humanized nursing course can enhance the compliance of the patients after the surgery, improve the quality of life, and inhibit the anxiety and depression of the patients, so it showed a positive effect on the neuroendocrine hormones and the prognosis of the patients.

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Automatic detection method of hepatocellular carcinomas using the non-rigid registration method of multi-phase liver CT images

Journal of X-Ray Science and Technology ◽

10.3233/xst-150487 ◽

2015 ◽

Vol 23 (3) ◽

pp. 275-288 ◽

Cited By ~ 6

Author(s):

Jeongjin Lee ◽

Kyoung Won Kim ◽

So Yeon Kim ◽

Juneseuk Shin ◽

Kyung Jun Park ◽

...

Keyword(s):

Detection Method ◽

Automatic Detection ◽

Ct Images ◽

Registration Method ◽

Rigid Registration ◽

Liver Ct ◽

Hepatocellular Carcinomas ◽

Multi Phase

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Computed Tomography Image Processing Analysis in COVID-19 Patient Follow-Up Assessment

Journal of Healthcare Engineering ◽

10.1155/2021/8869372 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Santiago Tello-Mijares ◽

Luisa Woo

Keyword(s):

Computed Tomography ◽

Clustering Algorithm ◽

Cross Validation ◽

Mean Shift ◽

Ground Glass Opacity ◽

Ct Images ◽

Second Order ◽

Border Region ◽

Pulmonary Parenchyma

The rapid worldwide spread of the COVID-19 pandemic has infected patients around the world in a short space of time. Chest computed tomography (CT) images of patients who are infected with COVID-19 can offer early diagnosis and efficient forecast monitoring at a low cost. The diagnosis of COVID-19 on CT in an automated way can speed up many tasks and the application of medical treatments. This can help complement reverse transcription-polymerase chain reaction (RT-PCR) diagnosis. The aim of this work is to develop a system that automatically identifies ground-glass opacity (GGO) and pulmonary infiltrates (PIs) on CT images from patients with COVID-19. The purpose is to assess the disease progression during the patient’s follow-up assessment and evaluation. We propose an efficient methodology that incorporates oversegmentation mean shift followed by superpixel-SLIC (simple linear iterative clustering) algorithm on CT images with COVID-19 for pulmonary parenchyma segmentation. To identify the pulmonary parenchyma, we described each superpixel cluster according to its position, grey intensity, second-order texture, and spatial-context-saliency features to classify by a tree random forest (TRF). Second, by applying the watershed segmentation to the mean-shift clusters, only pulmonary parenchyma segmentation-identified zones showed GGO and PI based on the description of each watershed cluster of its position, grey intensity, gradient entropy, second-order texture, Euclidean position to the border region of the PI zone, and global saliency features, after using TRF. Our classification results for pulmonary parenchyma identification on CT images with COVID-19 had a precision of over 92% and recall of over 92% on twofold cross validation. For GGO, the PI identification showed 96% precision and 96% recall on twofold cross validation.

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Prediction of Progression or Regression of Type A Aortic Intramural Hematoma by Computed Tomography

Circulation ◽

10.1161/circ.100.suppl_2.ii-281 ◽

1999 ◽

Vol 100 (suppl_2) ◽

Author(s):

Shuichiro Kaji ◽

Kazuhiro Nishigami ◽

Takashi Akasaka ◽

Takeshi Hozumi ◽

Tsutomu Takagi ◽

...

Keyword(s):

Computed Tomography ◽

Wall Thickness ◽

Aortic Wall ◽

Intramural Hematoma ◽

Type A ◽

Ct Images ◽

Aortic Diameter ◽

Aortic Wall Thickness ◽

Aortic Intramural Hematoma

Background —It has been reported that early surgery should be required for patients with type A aortic intramural hematoma (IMH) because it tends to develop classic aortic dissection or rupture. However, the anatomic features of type A IMH that develops dissection or rupture are unknown. The purpose of this study was to investigate the predictors of progression or regression of type A IMH by computed tomography (CT). Methods and Results —Twenty-two consecutive patients with type A IMH were studied by serial CT images. Aortic diameter and aortic wall thickness of the ascending aorta were estimated in CT images at 3 levels on admission and at follow-up (mean 37 days). We defined patients who showed increased maximum aortic wall thickness in the follow-up CT (n=9) or died of rupture (n=1) as the progression group (n=10). The other 12 patients, who all showed decreased maximum wall thickness, were categorized as the regression group. In the progression group, the maximum aortic diameter in the initial CT was significantly greater than that in the regression group (55±6 vs 47±3 mm, P =0.001). A Cox regression analysis revealed that the maximum aortic diameter was the strongest predictor for progression of type A IMH. We considered the optimal cutoff value to be 50 mm for the maximum aortic diameter to predict progression (positive predictive value 83%, negative predictive value 100%). Conclusions —Maximum aortic diameter estimated by the initial CT images is predictive for progression of type A IMH.

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Non-Rigid Registration of Liver CT Images for CT-Guided Ablation of Liver Tumors

PLoS ONE ◽

10.1371/journal.pone.0161600 ◽

2016 ◽

Vol 11 (9) ◽

pp. e0161600 ◽

Cited By ~ 11

Author(s):

Ha Manh Luu ◽

Camiel Klink ◽

Wiro Niessen ◽

Adriaan Moelker ◽

Theo van Walsum

Keyword(s):

Liver Tumors ◽

Ct Images ◽

Ct Guided ◽

Rigid Registration ◽

Liver Ct

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Prediction of Inflammatory Breast Cancer Survival Outcomes Using Computed Tomography-Based Texture Analysis

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.695305 ◽

2021 ◽

Vol 9 ◽

Author(s):

Sung Eun Song ◽

Bo Kyoung Seo ◽

Kyu Ran Cho ◽

Ok Hee Woo ◽

Balaji Ganeshan ◽

...

Keyword(s):

Breast Cancer ◽

Computed Tomography ◽

Standard Deviation ◽

Texture Analysis ◽

Inflammatory Breast Cancer ◽

Ct Images ◽

Chest Ct ◽

Invasive Technique ◽

Textural Features

Background: Although inflammatory breast cancer (IBC) has poor overall survival (OS), there is little information about using imaging features for predicting the prognosis. Computed tomography (CT)-based texture analysis, a non-invasive technique to quantify tumor heterogeneity, could be a potentially useful imaging biomarker. The aim of the article was to investigate the usefulness of chest CT-based texture analysis to predict OS in IBC patients.Methods: Of the 3,130 patients with primary breast cancers between 2006 and 2016, 104 patients (3.3%) with IBC were identified. Among them, 98 patients who underwent pre-treatment contrast-enhanced chest CT scans, got treatment in our institution, and had a follow-up period of more than 2 years were finally included for CT-based texture analysis. Texture analysis was performed on CT images of 98 patients, using commercially available software by two breast radiologists. Histogram-based textural features, such as quantification of variation in CT attenuation (mean, standard deviation, mean of positive pixels [MPP], entropy, skewness, and kurtosis), were recorded. To dichotomize textural features for survival analysis, receiver operating characteristic curve analysis was used to determine cutoff points. Clinicopathologic variables, such as age, node stage, metastasis stage at the time of diagnosis, hormonal receptor positivity, human epidermal growth factor receptor 2 positivity, and molecular subtype, were assessed. A Cox proportional hazards model was used to determine the association of textural features and clinicopathologic variables with OS.Results: During a mean follow-up period of 47.9 months, 41 of 98 patients (41.8%) died, with a median OS of 20.0 months. The textural features of lower mean attenuation, standard deviation, MPP, and entropy on CT images were significantly associated with worse OS, as was the M1 stage among clinicopathologic variables (all P-values < 0.05). In multivariate analysis, lower mean attenuation (hazard ratio [HR], 3.26; P = 0.003), lower MPP (HR, 3.03; P = 0.002), and lower entropy (HR, 2.70; P = 0.009) on chest CT images were significant factors independent from the M1 stage for predicting worse OS.Conclusions: Lower mean attenuation, MPP, and entropy on chest CT images predicted worse OS in patients with IBC, suggesting that CT-based texture analysis provides additional predictors for OS.

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More than six weeks to resolve from CT manifestations in COVID-19 patient: A case report

10.21203/rs.3.rs-20188/v1 ◽

2020 ◽

Author(s):

Wenxiong Xu ◽

Ziying Lei ◽

Dabiao Chen ◽

Xuejun Li ◽

Zhanlian Huang ◽

...

Keyword(s):

Computed Tomography ◽

Health Care Providers ◽

Ct Images ◽

Ct Imaging ◽

Chest Ct ◽

Care Providers ◽

Case Presentation ◽

Chest Computed Tomography ◽

Guangzhou City

Abstract Background: Coronavirus Disease 2019 (COVID-19) outbroke in Wuhan and spread to the world recently. But there were little studies on how long it took to recover from treatment beginning and resolve from chest computed tomography (CT) imaging so far.Case presentation: A patient diagnosed with severe type of COVID-19 was reported in this study. He was the first patient recovered and discharged from our hospital located in Guangzhou city. Initial chest computed tomography (CT) images of him showed bilateral multiple lobular peripheral ground-glass opacities without consolidation. Features and changes of his chest CT images from admission to discharge and follow-up were demonstrated. It took more than six weeks for lesion resolution in CT manifestations although the symptoms improved for a period of time after proper treatment. Conclusions: Repeated chest CT imaging for a period of more than six weeks in patients of COVID-19 is necessary to ascertain the lesion resolution and completely recovery. The result could be supplementary data to COVID-19 and help health care providers manage the COVID-19 patients.

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Multislice imaging of gastrointestinal stromal tumors

10.32512/jmr.1.1.2018/3.14 ◽

2018 ◽

pp. 3-14

Keyword(s):

Computed Tomography ◽

Key Words ◽

Gold Standard ◽

Gastrointestinal Stromal Tumors ◽

Mesenchymal Tumors ◽

Stromal Tumors ◽

Preferential Localization ◽

Multislice Imaging ◽

Computed Tomography Diagnosis

Gastrointestinal stromal tumors (GIST) are the most common mesenchymal tumors of the digestive tract (1%). These tumors express the CD 117 in 95% of cases. The stomach is the preferential localization (70%). Diagnosis is difficult and sometimes late. Progress of imaging has greatly improved the management and the prognosis. Computed tomography (CT) is the gold standard for diagnosis, staging, and treatment follow-up. The increasing recognition of GIST’s histopathology and the prolonged survival revealed some suggestive imaging aspects. Key words: gastro-intestinal stromal tumors; computed tomography; diagnosis

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