Learning size adaptive local maxima selection for robust nuclei detection in histopathology images

The detection of cells and nuclei is a crucial step for the automatic analysis of digital pathology slides and as such for the quantification of the phenotypic information contained in tissue sections. This task is however challenging because of high variability in size, shape and textural appearance of the objects to be detected and of the high variability of tissue appearance. In this work, we propose an approach to specifically tackle the variability in size. Modeling the detection problem as a local maxima detection problem on a center probabilistic map, we introduce a nuclear surface area map to guide the selection of local maxima while releasing a-priori knowledge on the size or structure of the objects to be detected. The good performance of our approach is quantitatively shown against state-of-the-art nuclei detection methods.