Robust cell particle detection to dense regions and subjective training samples based on prediction of particle center using convolutional neural network.

• Main Authors: Kenshiro Nishida, Kazuhiro Hotta
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2018-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC6179199?pdf=render

In recent years, finding the cause of pathogenesis is expected by observing the cell images. In this paper, we propose a cell particle detection method in cell images. However, there are mainly two kinds of problems in particle detection in cell image. The first is the different properties between cell images and standard images used in computer vision researches. Edges of cell particles are ambiguous, and overlaps between cell particles are often occurred in dense regions. It is difficult to detect cell particles by simple detection method using a binary classifier. The second is the ground truth made by cell biologists. The number of training samples for training a classifier is limited, and incorrect samples are included by the subjectivity of observers. From the background, we propose a cell particle detection method to address those problems. In our proposed method, we predict the center of a cell particle from the peripheral regions by convolutional neural network, and the prediction results are voted. By using the obvious peripheral edges, we can robustly detect overlapped cell particles because all edges of overlapping cell particles are not ambiguous. In addition, voting from peripheral views enables reliable detection. Moreover, our method is useful in practical applications because we can prepare many training samples from a cell particle. In experiments, we evaluate our detection methods on two kinds of cell detection datasets. One is challenging dataset for synthetic cells, and our method achieved the state-of-the-art performance. The other is real dataset of lipid droplets, and our method outperformed the conventional detector using CNN with binary outputs for particles and non-particles classification.