Inner Cell Mass and Trophectoderm Segmentation in Human Blastocyst Images using Deep Neural Network

TL;DR

This paper presents a deep neural network approach for accurately segmenting the inner cell mass and trophectoderm in human blastocyst images, aiding embryo viability assessment in IVF.

Contribution

It introduces a novel DNN-based segmentation method achieving high accuracy and robustness for ICM and TE regions in blastocyst images.

Findings

ICM segmentation accuracy of 99.1%

TE segmentation accuracy of 98.3%

High precision and Dice Coefficient scores

Abstract

Embryo quality assessment based on morphological attributes is important for achieving higher pregnancy rates from in vitro fertilization (IVF). The accurate segmentation of the embryo's inner cell mass (ICM) and trophectoderm epithelium (TE) is important, as these parameters can help to predict the embryo viability and live birth potential. However, segmentation of the ICM and TE is difficult due to variations in their shape and similarities in their textures, both with each other and with their surroundings. To tackle this problem, a deep neural network (DNN) based segmentation approach was implemented. The DNN can identify the ICM region with 99.1% accuracy, 94.9% precision, 93.8% recall, a 94.3% Dice Coefficient, and a 89.3% Jaccard Index. It can extract the TE region with 98.3% accuracy, 91.8% precision, 93.2% recall, a 92.5% Dice Coefficient, and a 85.3% Jaccard Index.