A minimally interactive method to segment enlarged lymph nodes in 3D thoracic CT images using a rotatable spiral-scanning technique

2012 ◽  
Author(s):  
Lei Wang ◽  
Jan H. Moltz ◽  
Lars Bornemann ◽  
Horst K. Hahn
Keyword(s):  
Lymph Nodes ◽  
Ct Images ◽  
Interactive Method ◽  
Spiral Scanning ◽  
Thoracic Ct ◽  
Scanning Technique

Automating the tracking of lymph nodes in follow-up studies of thoracic CT images

2012 ◽  
Vol 106 (3) ◽  
pp. 150-159 ◽  
Author(s):  
Peicong Yu ◽  
Kenneth Sheah ◽  
Chueh Loo Poh
Keyword(s):  
Lymph Nodes ◽  
Ct Images ◽  
Thoracic Ct ◽  
Follow Up Studies

scholarly journals Segmentation of Liver Metastases Using a Level Set Method with Spiral-Scanning Technique and Supervised Fuzzy Pixel Classification

2008 ◽  
Author(s):  
Dirk Smeets ◽  
Bert Stijnen ◽  
Dirk Loeckx ◽  
Bart De Dobbelaer ◽  
Paul Suetens
Keyword(s):  
Liver Metastases ◽  
Level Set ◽  
Liver Tumors ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Specific Method ◽  
Pixel Classification ◽  
Spiral Scanning ◽  
Level Set Function ◽  
Scanning Technique

In this paper a specific method is presented to facilitate the semi-automatic segmentation of liver metastases in CT images. Accurate and reliable segmentation of tumors is e.g. essential for the follow-up of cancer treatment. The core of the algorithm is a level set function. The initialization is provided by a spiral-scanning technique based on dynamic programming. The level set evolves according to a speed image that is the result of a statistical pixel classification algorithm with supervised learning. This method is tested on CT images of the abdomen and compared with manual delineations of liver tumors.

scholarly journals Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images

2009 ◽  
Vol 56 (7) ◽  
pp. 1810-1820 ◽  
Author(s):  
Xujiong Ye ◽  
Xinyu Lin ◽  
J. Dehmeshki ◽  
G. Slabaugh ◽  
G. Beddoe
Keyword(s):  
Ct Images ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Thoracic Ct ◽  
Computer Aided

scholarly journals Multi-factorial considerations for intra-thoracic lymph node evaluations of healthy cats on computed tomographic images

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Ninlawan Thammasiri ◽  
Chutimon Thanaboonnipat ◽  
Nan Choisunirachon ◽  
Damri Darawiroj
Keyword(s):  
Lymph Node ◽  
Lymph Nodes ◽  
Ct Images ◽  
Slice Thickness ◽  
Maximum Width ◽  
Post Contrast ◽  
Computed Tomographic ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Abstract Background It is difficult to examine mild to moderate feline intra-thoracic lymphadenopathy via and thoracic radiography. Despite previous information from computed tomographic (CT) images of intra-thoracic lymph nodes, some factors from animals and CT setting were less elucidated. Therefore, this study aimed to investigate the effect of internal factors from animals and external factors from the CT procedure on the feasibility to detect the intra-thoracic lymph nodes. Twenty-four, client-owned, clinically healthy cats were categorized into three groups according to age. They underwent pre- and post-contrast enhanced CT for whole thorax followed by inter-group evaluation and comparison of sternal, cranial mediastinal, and tracheobronchial lymph nodes. Results Post contrast-enhanced CT appearances revealed that intra-thoracic lymph nodes of kittens were invisible, whereas the sternal, cranial mediastinal, and tracheobronchial nodes of cats aged over 7 months old were detected (6/24, 9/24 and 7/24, respectively). Maximum width of these lymph nodes were 3.93 ± 0.74 mm, 4.02 ± 0.65 mm, and 3.51 ± 0.62 mm, respectively. By age, lymph node sizes of these cats were not significantly different. Transverse lymph node width of males was larger than that of females (P = 0.0425). Besides, the detection score of lymph nodes was affected by slice thickness (P < 0.01) and lymph node width (P = 0.0049). Furthermore, an irregular, soft tissue structure, possibly the thymus, was detected in all juvenile cats and three mature cats. Conclusions Despite additional information on intra-thoracic lymph nodes in CT images, which can be used to investigate lymphatic-related abnormalities, age, sex, and slice thickness of CT images must be also considered.

A new model-based framework for lung tissue segmentation in three-dimensional thoracic CT images

2017 ◽  
Vol 12 (2) ◽  
pp. 339-346 ◽  
Author(s):  
Zeinab Naseri Samaghcheh ◽  
Fatemeh Abdoli ◽  
Hamid Abrishami Moghaddam ◽  
Mohammadreza Modaresi ◽  
Neda Pak
Keyword(s):  
Lung Tissue ◽  
Three Dimensional ◽  
Ct Images ◽  
Tissue Segmentation ◽  
New Model ◽  
Model Based ◽  
Thoracic Ct

scholarly journals Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging

2019 ◽  
Author(s):  
Xue Sha ◽  
Guan Zhong Gong ◽  
Qing Tao Qiu ◽  
Jing Hao Duan ◽  
Deng Wang Li ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Ct Images ◽  
Ct Imaging ◽  
Arterial Phase ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph ◽  
Venous Phase

Abstract Background: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC). Methods: We selected 231 mediastinal LNs confirmed by pathology results as the subjects, which were divided into training (n=163) and validation cohorts (n=68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). Results: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. Conclusions: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

Automatic Artery-Vein Separation from Thoracic CT Images Using Integer Programming

2015 ◽  
pp. 36-43
Author(s):  
Christian Payer ◽  
Michael Pienn ◽  
Zoltán Bálint ◽  
Andrea Olschewski ◽  
Horst Olschewski ◽  
...  
Keyword(s):  
Integer Programming ◽  
Ct Images ◽  
Thoracic Ct
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