Infection with Mycobacterium tuberculosis (M.tb) results in immune cell recruitment to the lungs, forming macrophage-rich regions (granulomas) and lymphocyte-rich regions (lymphocytic cuffs). The objective of this study was to accurately identify and characterize these regions from hematoxylin and eosin (H&E)-stained tissue slides. The two target regions (granulomas and lymphocytic cuffs) can be identified by their morphological characteristics. Their most differentiating characteristic on H&E slides is cell density. We developed a computational framework, called DeHiDe, to detect and classify high cell-density regions in histology slides. DeHiDe employed a novel internuclei geodesic distance calculation and Dulmange Mendelsohn permutation to detect and classify high cell-density regions. Lung tissue slides of mice experimentally infected with M.tb were stained with H&E and digitized. A total of 21 digital slides were used to develop and train the computational framework. The performance of the framework was evaluated using two main outcome measures: correct detection of potential regions, and correct classification of potential regions into granulomas and lymphocytic cuffs. DeHiDe provided a detection accuracy of 99.39% while it correctly classified 90.87% of the detected regions for the images where the expert pathologist produced the same ground truth during the first and second round of annotations. We showed that DeHiDe could detect high cell-density regions in a heterogeneous cell environment with non-convex tissue shapes.
Keywords: Mycobacterium tuberculosis; geodesic distance; granulomas; internuclei distance; lung tissue; lymphocytic cuffs.
© 2013 International Society for Advancement of Cytometry.