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To optimize clinical outcomes, fertility clinics must strategically selectwhich embryos to transfer. Common selection heuristics are formulas expressedin terms of the durations required to reach various developmental milestones,quantities historically annotated manually by experienced embryologists basedon time-lapse EmbryoScope videos. We propose a new method for automatic embryostaging that exploits several sources of structure in this time-lapse data.First, noting that in each image the embryo occupies a small subregion, wejointly train a region proposal network with the downstream classifier toisolate the embryo. Notably, because we lack ground-truth bounding boxes, ourwe weakly supervise the region proposal network optimizing its parameters viareinforcement learning to improve the downstream classifier's loss. Moreover,noting that embryos reaching the blastocyst stage progress monotonicallythrough earlier stages, we develop a dynamic-programming-based decoder thatpost-processes our predictions to select the most likely monotonic sequence ofdevelopmental stages. Our methods outperform vanilla residual networks andrival the best numbers in contemporary papers, as measured by both per-frameaccuracy and transition prediction error, despite operating on smaller datathan many.

Embryo staging with weakly-supervised region selection and dynamically-decoded predictions