A fully automatic probabilistic 3D approach for the detection and assessment of pleural thickenings from CT data

Pleural thickenings are caused by asbestos exposure and may evolve into malignant pleural mesothelioma. The detection of pleural thickenings is today done by visual inspection of CT data, which is time-consuming and underlies the subjective judgment. In this work, thickenings are initially detected as the differences between the original contours and the healthy model of the pleura. A subsequent tissue-specific segmentation using the 3D Gibbs-Markov random field (GMRF) within the initially detected region-of-interest separates thickenings from thoracic tissue. Morphometric analysis leads then to 3D modeling and volumetric assessment. Both automatic detection and morphometric modeling of pleural thickenings proposed in this work assure not only reproducible detection but also precise measurement, hence this automated approach can assist physicians to diagnose pleural mesothelioma in its early stage.