A unified graphical models framework for automated mitosis detection in human embryos

Time lapse microscopy has emerged as an important modality for studying human embryo development, as mitosis events can provide insight into embryo health and fate. Mitosis detection can happen through tracking of embryonic cells (tracking based), or from low level image features and classifiers (tracking free). Tracking based approaches are challenged by high dimensional search space, weak features, outliers, missing data, multiple deformable targets, and weak motion model. Tracking free approaches are data driven and complement tracking based approaches. We pose mitosis detection as augmented simultaneous segmentation and classification in a conditional random field (CRF) framework that combines both approaches. It uses a rich set of discriminative features and their spatiotemporal context. It performs a dual pass approximate inference that addresses the high dimensionality of tracking and combines results from both components. For 312 clinical sequences we measured division events to within 30 min and observed an improvement of 25.6% and a 32.9% improvement over purely tracking based and tracking free approach respectively, and close to an order of magnitude over a traditional particle filter. While our work was motivated by human embryo development, it can be extended to other detection problems in image sequences of evolving cell populations.