Cell type-independent mitosis event detection via hidden-state conditional neural fields

This paper proposes a cell type-independent mitosis event detection method based on hidden-state conditional neural fields in time-lapse phase contrast microscopy sequences of stem cell populations. This method proceeds through three steps. First, we apply the imaging model-based microscopy image segmentation method and volumetric region growing to extract candidate sequences. Then, we extract the GIST feature of each frame within a candidate sequence for visual representation. Finally, a hidden-state conditional neural field classifier is trained to classify each candidate as mitosis or not. The main contribution is that the proposed method can jointly realize non-linear feature learning for different types of cells and temporal dynamic modeling of mitotic progression. The comparison experiments demonstrated the proposed method can benefit the detection of cell type-independent mitosis.