Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cleavage and Blastulation

Cleavage and Blastulation

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Adversarial CAM Guidance for Chest X-Ray Classification: Reducing Framing Sensitivity with Mask Supervision.

Biomimetics (Basel, Switzerland)·2026

Same author

Morphology-Aware Deep Features and Frozen Filters for Surgical Instrument Segmentation with LLM-Based Scene Summarization.

Journal of clinical medicine·2026

Same author

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

Diagnostics (Basel, Switzerland)·2026

Same author

KDOSS-net: Knowledge distillation-based outpainting and semantic segmentation network for crop and weed images.

Plant phenomics (Washington, D.C.)·2025

Same author

Automated Detection and Grading of Renal Cell Carcinoma in Histopathological Images via Efficient Attention Transformer Network.

Medical sciences (Basel, Switzerland)·2025

Same author

AS-YOLO: Enhanced YOLO Using Ghost Bottleneck and Global Attention Mechanism for Apple Stem Segmentation.

Sensors (Basel, Switzerland)·2025

Same journal

Precision Proteomic Profiling of Systemic Lupus Erythematosus-Correlating Disease Activity and Complement Levels with Clinical Phenotypes.

Biomedicines·2026

Same journal

The Role of Salivary Microbiota in Pancreatic Cancer: From Screening to Tumor Progression and Treatment Response.

Biomedicines·2026

Same journal

Diagnostic Utility of Surface Electromyography for Identifying Muscles Affected by Myofascial Trigger Points: A Scoping Review.

Biomedicines·2026

Same journal

Performance Assessment of a Locally Semi-Automated NGS-Based Workflow for Homologous Recombination Deficiency Testing in High-Grade Serous Ovarian Carcinoma.

Biomedicines·2026

Same journal

Coupling and Uncoupling Pleiotropy Between Hypertension and Type 2 Diabetes Contribute to Exploring Potential Heterogeneity in Cardiovascular Risk in East Asian Population.

Biomedicines·2026

Same journal

Maternal Response to Therapeutic Plasma Exchange in Early Gestation: A Case Series of Thrombotic Microangiopathies and Neurological Disorders.

Biomedicines·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 3, 2025

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

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

Published on: February 22, 2016

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

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

More Related Videos

Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

Published on: August 10, 2022

A Semi-high-throughput Imaging Method and Data Visualization Toolkit to Analyze C. elegans Embryonic Development

A Semi-high-throughput Imaging Method and Data Visualization Toolkit to Analyze C. elegans Embryonic Development

Published on: October 29, 2019

Related Experiment Videos

Last Updated: Sep 3, 2025

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

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

Published on: February 22, 2016

Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

Published on: August 10, 2022

A Semi-high-throughput Imaging Method and Data Visualization Toolkit to Analyze C. elegans Embryonic Development

A Semi-high-throughput Imaging Method and Data Visualization Toolkit to Analyze C. elegans Embryonic Development

Published on: October 29, 2019

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.