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

Source: [Automated Measurements of Key Morphological Features of Human Embryos for IVF](<https://www.sota2.com/research/paper/68fa5eff1e209131ce9e6c92>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:05:52.386Z

Authors: Brian D. Leahy, Won-Dong Jang, Helen Y. Yang, Robbert Struyven, Donglai Wei, Zhe Sun, Kylie R. Lee, Charlotte Royston, Liz Cam, Yael Kalma, Foad Azem, Dalit Ben-Yosef, Hanspeter Pfister, Daniel Needleman

Published: 2020-05-29T20:27:17.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Developmental Stage Classification on Human embryo dataset (test)](<https://www.sota2.com/research/sota/developmental-stage-classification-on-human-embryo-dataset-test.md>): Developmental Stage Classification; Human embryo dataset (test); Global Accuracy: 77.8.

- [Embryo Stage Classification on NYU Mouse Embryo 2 (test)](<https://www.sota2.com/research/sota/embryo-stage-classification-on-nyu-mouse-embryo-2-test.md>): Embryo Stage Classification; NYU Mouse Embryo 2 (test); Global Accuracy: 0.964.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.00067>)

[PDF](<https://arxiv.org/pdf/2006.00067.pdf>)