Tracking interval changes of pulmonary nodules using a sequence of three-dimensional thoracic images

2000 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Omatsu ◽  
Masahiko Kusumoto ◽  
Ryutaro Kakinuma ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules

scholarly journals Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

2020 ◽  
Vol 10 (7) ◽  
pp. 2346 ◽  
Author(s):  
May Phu Paing ◽  
Kazuhiko Hamamoto ◽  
Supan Tungjitkusolmun ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Random Forest ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Region Growing ◽  
Favorable Result ◽  
Dimensional Chain ◽  
Chain Code ◽  
Seeded Region Growing ◽  
Chain Coding ◽  
Diagnosis Of Lung Cancer

The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.

scholarly journals Preoperative virtual simulation for synchronous multiple primary lung cancers using three-dimensional computed tomography lung reconstruction: a case report

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Weichun Wu ◽  
Yimin Wu ◽  
Gang Shen ◽  
Guofei Zhang
Keyword(s):  
Computed Tomography ◽  
Thoracoscopic Surgery ◽  
Three Dimensional ◽  
Primary Lung Cancer ◽  
Pulmonary Nodules ◽  
Term Survival ◽  
Lung Cancers ◽  
Multiple Primary ◽  
Feasible Alternative

Abstract Background As the positions and sizes of nodules in synchronous multiple primary lung cancer (SMPLC) patients differ, the development of surgical strategies to maximize long-term survival and preserved postoperative pulmonary function in SMPLC patients for whom surgical resection is an alternative strategy presents challenges. Case presentation We provide a case managed through video-assisted thoracoscopic surgery (VATS) resection using three-dimensional computed tomography lung reconstruction (3D-CTLR) to reconstruct lobes containing pulmonary nodules to preoperatively simulate and intraoperatively guide the extent and method of resection. Conclusion The successful attempt demonstrates a technically simplified, feasible alternative to preoperative plans utilizing less invasive VATS to manage SMPLC.

scholarly journals AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

2017 ◽  
Vol 36 (2) ◽  
pp. 65 ◽  
Author(s):  
Elaheh Aghabalaei Khordehchi ◽  
Ahmad Ayatollahi ◽  
Mohammad Reza Daliri
Keyword(s):  
Lung Nodule ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Region Merging ◽  
Three Dimensional Objects ◽  
Ct Data ◽  
Statistical Region Merging

Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

Pulmonary Nodules: Preliminary Experience with Three-dimensional Evaluation

Radiology ◽  
2004 ◽  
Vol 231 (2) ◽  
pp. 459-466 ◽  
Author(s):  
Marie-Pierre Revel ◽  
Catherine Lefort ◽  
Alvine Bissery ◽  
Marie Bienvenu ◽  
Laetitia Aycard ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules

Small Pulmonary Nodules: Reproducibility of Three-dimensional Volumetric Measurement and Estimation of Time to Follow-up CT

Radiology ◽  
2004 ◽  
Vol 231 (2) ◽  
pp. 446-452 ◽  
Author(s):  
William J. Kostis ◽  
David F. Yankelevitz ◽  
Anthony P. Reeves ◽  
Simina C. Fluture ◽  
Claudia I. Henschke
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Volumetric Measurement ◽  
Small Pulmonary Nodules ◽  
Estimation Of Time

scholarly journals Three-Dimensional Detection of Pulmonary Nodules in Chest CT Images

2011 ◽  
Vol 4 (5) ◽  
Author(s):  
Aliaa A. A. Youssif ◽  
Shereen A. Hussein ◽  
Ahmed S. Ibrahim
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Chest Ct

Characterization of solid pulmonary nodules using three-dimensional features

2007 ◽  
Author(s):  
Artit C. Jirapatnakul ◽  
Anthony P. Reeves ◽  
Tatiyana V. Apanasovich ◽  
Matthew D. Cham ◽  
David F. Yankelevitz ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules

scholarly journals Three-dimensional navigation-guided thoracoscopic combined subsegmentectomy for intersegmental pulmonary nodules

2018 ◽  
Vol 10 (1) ◽  
pp. 41-46 ◽  
Author(s):  
Wei-Bing Wu ◽  
Yang Xia ◽  
Xiang-Long Pan ◽  
Jun Wang ◽  
Zhi-Cheng He ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules
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