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