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The timing of cell divisions in early embryos during the In-VitroFertilization (IVF) process is a key predictor of embryo viability. However,observing cell divisions in Time-Lapse Monitoring (TLM) is a time-consumingprocess and highly depends on experts. In this paper, we propose EmbryosFormer,a computational model to automatically detect and classify cell divisions fromoriginal time-lapse images. Our proposed network is designed as anencoder-decoder deformable transformer with collaborative heads. Thetransformer contracting path predicts per-image labels and is optimized by aclassification head. The transformer expanding path models the temporalcoherency between embryo images to ensure monotonic non-decreasing constraintand is optimized by a segmentation head. Both contracting and expanding pathsare synergetically learned by a collaboration head. We have benchmarked ourproposed EmbryosFormer on two datasets: a public dataset with mouse embryoswith 8-cell stage and an in-house dataset with human embryos with 4-cell stage.Source code: https://github.com/UARK-AICV/Embryos.

EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification