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oai:repisalud.isciii.es:20.500.12105/65742024-09-27T10:16:21Zcom_20.500.12105_19604com_20.500.12105_2051col_20.500.12105_19607

00925njm 22002777a 4500 dc Gordaliza, Pedro M author Muñoz-Barrutia, Arrate author Abella, Monica author Desco, Manuel author Sharpe, Sally author Vaquero, Juan Jose author 2018 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. Sci Rep. 2018; 8(1):9802 10.1038/s41598-018-28100-x 2045-2322 Scientific Reports 29955159 http://hdl.handle.net/20.500.12105/6574 DISEASE PROGRESSION LATENT TUBERCULOSIS LEVEL ALGORITHMS REGRESSION DIAGNOSIS FRAMEWORK MACAQUES ACCURATE NODULES Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model