Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Development of an automated two pronuclei detection system on time‐lapse embryo images using deep learning techniques

2 Jun 2020

Abstract excerpt

PURPOSE: To establish an automated pronuclei determination system by analysis using deep learning technology which is able to effectively learn with limited amount of supervised data. METHODS: An algorithm was developed by explicitly incorporating human observation where the outline around pronuclei is being observed in determining the number of pronuclei. Supervised data were selected from the time-lapse images...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Ovarian function and disorders
Share to Prenatal Screening and Diagnostics
Share to Reproductive Biology and Fertility

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Identifiers

PMID: 32684828
DOI: 10.1002/rmb2.12331

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 12 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Development of an automated two pronuclei detection system on time‐lapse embryo images using deep learning techniquesPMID 32684828Read ↗

Variation of female pronucleus reveal oocyte or embryo abnormality: An expert experience deep learning of non-dark box analysisSemantic proximity Read ↗

Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐LearningSemantic proximity Read ↗

Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A CyclesSemantic proximity Read ↗

Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy predictionSemantic proximity Read ↗

Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro FertilizationSemantic proximity Read ↗

A clinical consensus-compliant deep learning approach to quantitatively evaluate human in vitro fertilization early embryonic development with optical microscope imagesSemantic proximity Read ↗

Automatic Ploidy Prediction and Quality Assessment of Human Blastocyst Using Time-Lapse ImagingSemantic proximity Read ↗

Embryo ploidy status classification through computer-assisted morphology assessmentSemantic proximity Read ↗

A predictive model for blastocyst formation based on morphokinetic parameters in time-lapse monitoring of embryo developmentSemantic proximity Read ↗

Development and validation of deep learning based embryo selection across multiple days of transferSemantic proximity Read ↗

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by ageSemantic proximity Read ↗

Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro FertilizationSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.