Spatiotemporal mitosis event detection in time-lapse phase contrast microscopy image sequences

High-throughput automated analysis of cell population behaviors in vitro is of great importance to biological research. In particular, automated quantification of cellular mitosis in time-lapse microscopy video is useful for multiple applications such as tissue engineering, cancer research, and developmental biology. Accurate localization and counting of mitosis are challenging since cells undergo drastic morphological and appearance changes during mitosis. To tackle this challenge, we propose a fully-automated detection method for cells imaged with phase contrast microscopy. The method consists of three stages: image preconditioning, spatiotemporal volume extraction and SVM - based mitosis event detection. First, the input images are transformed based on physics of phase contrast image formation such that potential mitosis regions are assigned high values. Second, volumetric region grow was performed on the transformed images to extract candidate mitosis regions. Third, mitosis events are detected in the candidates using a Support Vector Machine (SVM) classifier. The proposed method does not depend on empirical parameters, ad hoc image processing, or explicit cell tracking; and can be straightforwardly adapted to different cell types. It was validated with 10 image sequences consisting of 8000 images, and achieved excellent performance with 90.6% average precision and 95.6% average recall.