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

The timing of cell divisions in early embryos during 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 label and is optimized by a classification head. The transformer expanding path models the temporal coherency between embryos 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.

1. Installation

• we use PyTorch 1.12.1 and cuda 11.3 (higher versions may be available)

2. Dataset preparation

• Our proposed Human Embryos dataset can be downloaded at: google_drive

3. Training and Validation

• Use scripts: scripts/train.sh and scripts/test.sh. Config file is in cfgs folder

Citation

Please consider citing this project in your publications if it helps your research.

The code is used for academic purpose only.

Acknowledgement

• The Transformer-based network is mainly based on PDVC and Deformable DETR. We thank the authors for their great works