A novel cell detection method using deep convolutional neural network and maximum-weight independent set

Abstract

Cell detection is an important topic in biomedical image analysis and it is often the prerequisite for the following segmentation or classification procedures. In this paper, we propose a novel algorithm for general cell detection problem: Firstly, a set of cell detection candidates is generated using different algorithms with varying parameters. Secondly, each candidate is assigned a score by a trained deep convolutional neural network (DCNN). Finally, a subset of best detection results are selected from all candidates to compose the final cell detection results. The subset selection task is formalized as a maximum-weight independent set problem, which is designed to find the heaviest subset of mutually non-adjacent nodes in a graph. Experiments show that the proposed general cell detection algorithm provides detection results that are dramatically better than any individual cell detection algorithm.