Tien-Phat Nguyen1,4, Trong-Thang Pham*4, Tri Nguyen**7, Hieu Le*1, Dung Nguyen5, Hau Lam6, Phong Nguyen1, Jennifer Fowler8, Minh-Triet Tran2,3,4, Ngan Le9
Published: February 9, 2023
1 FPT Software AI Center, Ho Chi Minh City, Vietnam
2 University of Science, VNU-HCM;
3 Vietnam National University, Ho Chi Minh City, Vietnam
4 John von Neumann Institute, Vietnam National University, Ho Chi Minh City, Vietnam
5 IVFMD, My Duc Phu Nhuan hospital, Ho Chi Minh City, Vietnam
6 Olea Fertility, Vinmec Central Park International Hospital, Ho Chi Minh City, Vietnam
7 HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
8 Arkansas Economic Development Commission, Little Rock, AR USA 72202
9 Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR, USA 72703
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.