# Automated Measurements of Key Morphological Features of Human Embryos for IVF

arXiv: https://arxiv.org/abs/2006.00067 · HTML: https://mlsanity.com/paper/2006.00067/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 8 of 8)

- [EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification](https://mlsanity.com/paper/2210.04615/) (2022)
- [Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field](https://mlsanity.com/paper/2107.06360/) (2021)
- [Multimodal Learning for Embryo Viability Prediction in Clinical IVF](https://mlsanity.com/paper/2410.15581/) (2024)
- [Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization](https://mlsanity.com/paper/2105.00060/) (2021)
- [Stain-free Detection of Embryo Polarization using Deep Learning](https://mlsanity.com/paper/2111.05315/) (2021)
- [Supervised contrastive learning for cell stage classification of animal embryos](https://mlsanity.com/paper/2502.07360/) (2025)
- [Spatial-Temporal Pre-Training for Embryo Viability Prediction Using Time-Lapse Videos](https://mlsanity.com/paper/2506.17403/) (2025)
- [Cytoplasmic Strings Analysis in Human Embryo Time-Lapse Videos using Deep Learning Framework](https://mlsanity.com/paper/2512.09461/) (2025)

## Abstract (arXiv)

A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy. Time-lapse microscopy provides clinicians with a wealth of information for selecting embryos. However, the resulting movies of embryos are currently analyzed manually, which is time consuming and subjective. Here, we automate feature extraction of time-lapse microscopy of human embryos with a machine-learning pipeline of five convolutional neural networks (CNNs). Our pipeline consists of (1) semantic segmentation of the regions of the embryo, (2) regression predictions of fragment severity, (3) classification of the developmental stage, and object instance segmentation of (4) cells and (5) pronuclei. Our approach greatly speeds up the measurement of quantitative, biologically relevant features that may aid in embryo selection.