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Gordaliza PM, Muñoz-Barrutia A, Abella M, Desco M, Sharpe S, Vaquero JJ. Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model. Sci Rep 2018; 8:9802. [PMID: 29955159 PMCID: PMC6023884 DOI: 10.1038/s41598-018-28100-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 06/12/2018] [Indexed: 02/06/2023] Open
Abstract
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts' annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94% ± 4%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm ± 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden.
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Affiliation(s)
- Pedro M Gordaliza
- Universidad Carlos III de Madrid, Departamento de Bioingeniería e Ingeniería Aeroespacial, Leganés, ES28911, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, ES28007, Spain
| | - Arrate Muñoz-Barrutia
- Universidad Carlos III de Madrid, Departamento de Bioingeniería e Ingeniería Aeroespacial, Leganés, ES28911, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, ES28007, Spain
| | - Mónica Abella
- Universidad Carlos III de Madrid, Departamento de Bioingeniería e Ingeniería Aeroespacial, Leganés, ES28911, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, ES28007, Spain
- Centro de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain
| | - Manuel Desco
- Universidad Carlos III de Madrid, Departamento de Bioingeniería e Ingeniería Aeroespacial, Leganés, ES28911, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, ES28007, Spain
- Centro de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, ES28029, Spain
| | - Sally Sharpe
- Public Health England, Microbiology Services Division, Porton Down, SP4 0JG, England
| | - Juan José Vaquero
- Universidad Carlos III de Madrid, Departamento de Bioingeniería e Ingeniería Aeroespacial, Leganés, ES28911, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, ES28007, Spain.
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Feng Y, Dong F, Xia X, Hu CH, Fan Q, Hu Y, Gao M, Mutic S. An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images. Med Phys 2017; 44:3752-3760. [PMID: 28513858 DOI: 10.1002/mp.12350] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 04/24/2017] [Accepted: 05/10/2017] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Ultrasound (US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis (CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. METHODS To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means (FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. RESULTS Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 ± 5.33%, 97.83 ± 2.17%, 86.38 ± 5.80%, and 92.58 ± 3.68%, respectively. The region-based metrics of average symmetric surface distance (ASSD), root mean square symmetric distance (RMSD), and maximum symmetric surface distance (MSSD) were 0.03 ± 0.04 mm, 0.04 ± 0.03 mm, and 1.18 ± 1.01 mm, respectively. All the metrics except sensitivity were better than that of the non-adaptive algorithm and the conventional FCM. Only three region-based metrics were better than that of the kernel-induced distance based FCM with spatial constraints. CONCLUSION Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the potential application of the method in breast tumor CAD and other US-guided procedures.
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Affiliation(s)
- Yuan Feng
- Center for Molecular Imaging and Nuclear Medicine, School of Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, Jiangsu, 215123, China.,School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, 215021, China.,School of Computer Science and Engineering, Soochow University, Suzhou, Jiangsu, 215021, China
| | - Fenglin Dong
- Department of Ultrasounds, the First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, 215006, China
| | - Xiaolong Xia
- Center for Molecular Imaging and Nuclear Medicine, School of Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, Jiangsu, 215123, China
| | - Chun-Hong Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, 215006, China
| | - Qianmin Fan
- Department of Ultrasounds, the First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, 215006, China
| | - Yanle Hu
- Department of Radiation Oncology, Mayo Clinic in Arizona, Phoenix, AZ, USA
| | - Mingyuan Gao
- Center for Molecular Imaging and Nuclear Medicine, School of Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, Jiangsu, 215123, China
| | - Sasa Mutic
- Department of Radiation Oncology, Washington University, St. Louis, MO, USA
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