Abstract
Robust and accurate nuclei localization in microscopy image can provide crucial clues for accurate computer-aid diagnosis. In this paper, we propose a convolutional neural network (CNN) based hough voting method to localize nucleus centroids with heavy cluttering and morphologic variations in microscopy images. Our method, which we name as deep voting, mainly consists of two steps. (1) Given an input image, our method assigns each local patch several pairs of voting offset vectors which indicate the positions it votes to, and the corresponding voting confidence (used to weight each votes), our model can be viewed as an implicit hough-voting codebook. (2) We collect the weighted votes from all the testing patches and compute the final voting density map in a way similar to Parzen-window estimation. The final nucleus positions are identified by searching the local maxima of the density map. Our method only requires a few annotation efforts (just one click near the nucleus center). Experiment results on Neuroendocrine Tumor (NET) microscopy images proves the proposed method to be state-of-the-art.