Unsupervised CT lung image segmentation of a mycobacterium tuberculosis infection model

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 identifcation 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 afected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extractionof 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 themajority voting of three experts' annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefcient, 94%±4%) and the surface similarity coefcients (Hausdorf distance, 8.64mm±7.36mm) in the majority of the most difcult-to-segment slices. Results indicate that the refned lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden.

Unsupervised CT lung image segmentation of a mycobacterium tuberculosis infection model Articles