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Related Experiment Video

Updated: Sep 22, 2025

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo
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A time-lapse embryo dataset for morphokinetic parameter prediction.

Tristan Gomez1, Magalie Feyeux2, Justine Boulant3

  • 1Nantes Université, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France.

Data in Brief
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

A new public dataset of 704 time-lapse imaging (TLI) videos aids artificial intelligence (AI) development for In Vitro Fertilization (IVF). This resource enhances embryo assessment, aiming to improve IVF success rates for infertile patients.

Keywords:
Computer visionDeep learningHuman reproductionIVFTime-lapseVideos

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Area of Science:

  • Reproductive Medicine
  • Biomedical Imaging
  • Artificial Intelligence

Background:

  • In Vitro Fertilization (IVF) is a common treatment for infertility.
  • Time-lapse imaging incubators (TLI) offer dynamic monitoring of embryo development.
  • Artificial Intelligence (AI), particularly deep learning (DL), shows promise for improving IVF embryo assessment.

Purpose of the Study:

  • To address the lack of public reference datasets for training and evaluating AI models in IVF.
  • To introduce a comprehensive, fully annotated dataset of TLI videos for embryo development.
  • To facilitate the advancement of DL-powered IVF.

Main Methods:

  • Creation of a dataset comprising 704 fully annotated TLI videos of developing embryos.
  • Inclusion of all 7 focal planes, totaling 2.4 million images.
  • Detailed annotations covering 16 distinct developmental phases, including novel early and late stages.

Main Results:

  • The dataset represents the first public resource for evaluating morphokinetic models in IVF.
  • It provides a foundation for developing and validating DL algorithms for embryo assessment.
  • The detailed annotations capture previously unused developmental phases.

Conclusions:

  • This dataset is a crucial step towards deep learning-powered IVF.
  • It is expected to enhance the performance of AI models in analyzing TLI videos.
  • Ultimately, this advancement aims to improve clinical success rates for infertile patients undergoing IVF.