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After a large-single-celled zygote is produced via fertilization, the process of cleavage occurs while zygotes travel through the uterine tube. Cleavage is a mitotic cell division that does not result in growth. With each round of successive cell division, daughter cells get increasingly smaller.

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Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network For Embryonic Analysis.

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Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network For Embryonic Analysis.

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Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic

Muhammad Arsalan¹, Adnan Haider¹, Se Woon Cho¹

¹Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea.

|July 27, 2022

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces MASS-Net, a deep learning model for automated analysis of human blastocysts in in vitro fertilization (IVF). It accurately identifies key embryonic components, improving viability prediction for infertility treatments.

Keywords:

embryo human blastocyst in vitro fertilization infertility semantic segmentation

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Area of Science:

Assisted Reproductive Technology
Medical Imaging Analysis
Deep Learning in Embryology

Background:

Infertility affects a significant global population, necessitating advanced reproductive techniques like in vitro fertilization (IVF).
Accurate assessment of human blastocyst morphology is crucial for predicting IVF success, traditionally relying on manual microscopic analysis.
Existing automated methods for blastocyst component analysis lack accuracy and require extensive preprocessing.

Purpose of the Study:

To develop a novel deep learning model for automated, accurate segmentation of human blastocyst components.
To improve the efficiency and reliability of morphological analysis in IVF procedures.
To address the limitations of current automated blastocyst analysis techniques.

Main Methods:

Proposed a novel multiscale aggregation semantic segmentation network (MASS-Net) combining four scales via depth-wise concatenation.
Utilized depth-wise separable convolutions to reduce trainable parameters, employing an innovative multiscale design for rich spatial information.
Evaluated MASS-Net on publicly available human blastocyst microscopic imaging data.

Main Results:

MASS-Net accurately detected trophectoderm (TE), zona pellucida (ZP), inner cell mass (ICM), and blastocoel (BL) with high mean Jaccard indices (79.08% - 89.28%).
The model achieved superior performance compared to state-of-the-art methods without requiring preprocessing stages.
Demonstrated the capability to provide simultaneous binary masks for each component, aiding embryological analysis.

Conclusions:

MASS-Net offers an accurate and efficient automated solution for human blastocyst component segmentation in IVF.
The proposed network architecture effectively captures spatial information across multiple resolutions, enhancing analytical performance.
This advancement has the potential to significantly improve IVF outcomes through more reliable embryonic viability prediction.