Mitosis detection in phase contrast microscopy image sequences using spatial segmentation and spatio-temporal localization refinement

Mitosis identification and localization gives us information on cell status and behaviour, and is foundational to many biological research and medical applications. In this paper, we propose a novel two-stage computer vision framework for the automation of mitosis event detection in large-scale time-lapse phase contrast microscopy image sequences. First stage is a two dimensional image segmentation method for proposing probable spatial locations of mitotic events. The second stage is spatio-temporal mitotic localization refinement driven image sequence classification. Second stage analyzes the temporal dynamics augmented with visual contextual learning and is specifically trained for classifying true mitotic events from hard negative cases wherein the cells appear very similar to mitotic cells. To the best of our knowledge, the proposed method outperforms all previous approaches of mitosis key-point detection. Our state of the art approach achieved top performance in the CVPR 2019 contest on mitosis detection in phase contrast microscopy image sequences with an average F1 score of 0.8804 on the test data of C2C12-16 dataset.