Automated Measurements of Key Morphological Features of Human Embryos for IVF

TL;DR

This paper presents an automated machine learning pipeline using CNNs to extract key morphological features from time-lapse microscopy of human embryos, aiming to improve IVF embryo selection.

Contribution

It introduces a novel five-component CNN-based pipeline for automated, rapid, and objective analysis of embryo images, replacing manual assessment.

Findings

Significantly speeds up embryo feature measurement.

Provides accurate segmentation and classification of embryo regions.

Potential to enhance embryo selection process in IVF.

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.