Mitosis Detection in Phase Contrast Microscopy Image Sequences of Stem Cell Populations: A Critical Review

Abstract

Detecting mitosis from cell population is a fundamental problem in many biological researches and biomedical applications. In modern researches, advanced imaging technologies have been applied to generate large amount of microscopy images of cells. However, detecting all mitotic cells from these images with human eye is tedious and time-consuming. In recent years, several approaches have been proposed to help humans finish this job automatically with high efficiency and accuracy. In this review paper, we first described some commonly used datasets for mitosis detection, and then discussed different kinds of methods for mitosis detection, like tracking based methods, tracking free methods, hybrid methods, and the most recently proposed works based on deep learning architecture. We compared these methods on same datasets, and found that deep learning based approaches have achieved a great improvement in performance. At last, we discussed the future possible approaches on mitosis detection, to combine the success of previous works and the advantage of big data in modern researches. Considering expertise is highly required in biomedical area, we will further discuss the possibility to learn information from biomedical big data with less expert annotation.