Nuclei Detection in Histopathological Images with Deep Learning and Heuristic Optimization

Abstract

Pathological examinations play a critical role in the diagnosis process. Pathologists analyze biopsies to make a diagnosis. However, detection of nuclei in histopathology images is a costly procedure in terms of time. Because of the complexity of histopathology images, different observers might reach different conclusions. Recently, automatic digital pathology, which is faster therefore beneficial for patients and pathologists, draw many attention for research and clinical practice. In comparison to manual image analysis, computerized methods are not affected by the inter-observer variations. In this paper, we automated the nuclei detection process using deep convolutional neural networks (CNN) and simulated annealing to find center coordinates of nuclei in hematoxylin and eosin (H&E) stained histopathology images of colorectal adenocarcinoma.