Automated Measurements of Key Morphological Features of Human Embryos for IVF

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...

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Bibliographic Details
Published in:arXiv.org 2020-07
Main Authors: Leahy, Brian D, Won-Dong, Jang, Yang, Helen Y, Struyven, Robbert, Donglai Wei, Sun, Zhe, Lee, Kylie R, Royston, Charlotte, Cam, Liz, Kalma, Yael, Azem, Foad, Ben-Yosef, Dalit, Pfister, Hanspeter, Needleman, Daniel
Format: Article
Language:English
Subjects:
Artificial neural networks
Automation
Embryos
Feature extraction
Machine learning
Microscopy
Regression analysis
Semantic segmentation
Online Access:Get full text
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Summary: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.
ISSN:2331-8422