Automated Quantification of Trophectoderm Morphology in Human Blastocysts via Instance Segmentation

Segmenting individual trophectoderm (TE) cells is essential for developing quantitative metrics to assess the developmental potential of human blastocysts. The elongated shape and circular arrangement of TE cells lead to continuously varying orientations across the image, posing challenges for existing cell instance segmentation methods that assume uniformly oriented cells. As a result, most methods segment the TE as a single region, and the development of quantitative, cell-level metrics predictive of live birth potential remains unexplored. In this work, we propose an instance segmentation model that represents elongate, circularly arranged TE cells using elliptical distance maps, with which superior performance in both segmentation accuracy and metric extraction was achieved, compared with state-of-the-art methods. The extracted metrics, including TE cell number, the mean and standard deviation of cell length, width, area, and mean inter-cell distance, serve as effective predictors of a blastocyst’s live birth potential. When used to predict live birth, these metrics achieved a significantly higher area under the receiver operating characteristic curve (AUC = 0.693) than traditional TE morphological grades (AUC = 0.585). The source code is publicly available at https://github.com/robotVisionHang/TESeg.