Particle detection in crowd regions using cumulative score of CNN

Abstract

In recent years, convolutional neural network gave the state-of-the-art performance on various image recognition benchmarks. Although CNN requires a large number of training images including various locations and sizes of a target, we cannot prepare a lot of supervised intracellular images. In addition, the properties of intracellular images are different from standard images used in computer vision researches. Overlap between particles often occurred in dense regions. In overlapping area, there are ambiguous edges at the peripheral region of particles. This induces the detection error by the conventional method. However, all edges of overlapping particles are not ambiguous. We should use the obvious peripheral edges. Thus, we try to predict the center of a particle from the peripheral regions by CNN, and the prediction results are voted. Since the particle center is predicted from peripheral views, we can prepare many training samples from one particle. High accuracy is obtained in comparison with the conventional binary detector using CNN as a binary classifier.