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Quantitative Analysis of Protein Expression to Study Lineage Specification in Mouse Preimplantation Embryos

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Published on: February 22, 2016

MiMics-Net: A Multimodal Interaction Network for Blastocyst Component Segmentation.

Adnan Haider¹, Muhammad Arsalan², Kyungeun Cho¹

¹Department of Computer Science and Artificial Intelligence, College of Advanced Convergence Engineering, Dongguk University-Seoul, 30 Pildongro 1-gil, Jung-gu, Seoul 04620, Republic of Korea.

Diagnostics (Basel, Switzerland)

|February 27, 2026

Summary

This summary is machine-generated.

A new AI model, MiMics-Net, accurately segments human blastocyst components for improved in vitro fertilization (IVF) success. This advanced segmentation overcomes limitations of current methods, offering a more reliable assessment for predicting pregnancy outcomes.

Keywords:

artificial intelligence blastocyst segmentation medical image analysis multimodal segmentation semantic segmentation

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Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

Published on: August 10, 2022

Area of Science:

Reproductive medicine
Artificial intelligence in healthcare
Medical image analysis

Background:

Global infertility rates are rising, increasing demand for effective assisted reproductive technologies.
In vitro fertilization (IVF) success relies heavily on accurate blastocyst assessment, a currently manual, subjective, and error-prone process.
Existing AI segmentation methods struggle with challenging blastocyst image conditions like low contrast and texture similarity.

Purpose of the Study:

To develop a novel AI-based segmentation network for accurate blastocyst component analysis.
To address limitations of current methods in handling complex image data and improve IVF success prediction.

Main Methods:

Developed MiMics-Net, a lightweight multimodal interaction segmentation network.
Utilized a multimodal blastocyst stem processing photometric intensity, local textures, and directional orientation.
Incorporated dual-path grouped blocks and a lightweight refinement decoder for enhanced feature processing and spatial restoration.

Main Results:

MiMics-Net achieved a Jaccard index score of 87.9% on a human blastocyst dataset.
The model requires only 0.65 million trainable parameters, indicating computational efficiency.
The multimodal approach and network architecture improved segmentation performance over existing methods.

Conclusions:

MiMics-Net offers a promising AI solution for accurate blastocyst segmentation in IVF.
The developed network effectively handles challenging image conditions, paving the way for more reliable IVF outcomes.
This lightweight and efficient model can enhance the precision of assisted reproductive technologies.