AIMC Topic: Time-Lapse Imaging
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Towards the automation of early-stage human embryo development detection.
Biomedical engineering online
Dec 12, 2019
BACKGROUND: Infertility and subfertility affect a significant proportion of humanity. Assisted reproductive technology has been proven capable of alleviating infertility issues. In vitro fertilisation is one such option whose success is highly depend...
Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.
Lab on a chip
Nov 22, 2019
Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy analysis done by embryologists or semi-automated time-lapse imaging systems are highly sub...
Exploiting open source 3D printer architecture for laboratory robotics to automate high-throughput time-lapse imaging for analytical microbiology.
PloS one
Nov 19, 2019
Growth in open-source hardware designs combined with the low-cost of high performance optoelectronic and robotics components has supported a resurgence of in-house custom lab equipment development. We describe a low cost (below $700), open-source, fu...
Automatic grading of human blastocysts from time-lapse imaging.
Computers in biology and medicine
Oct 15, 2019
BACKGROUND: Blastocyst morphology is a predictive marker for implantation success of in vitro fertilized human embryos. Morphology grading is therefore commonly used to select the embryo with the highest implantation potential. One of the challenges,...
Sequential Saliency Guided Deep Neural Network for Joint Mitosis Identification and Localization in Time-Lapse Phase Contrast Microscopy Images.
IEEE journal of biomedical and health informatics
Sep 23, 2019
The analysis of cell mitotic behavior plays important role in many biomedical research and medical diagnostic applications. To improve the accuracy of mitosis detection in automated analysis systems, this paper proposes the sequential saliency guided...
Predicting the future direction of cell movement with convolutional neural networks.
PloS one
Sep 4, 2019
Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell sta...
Phase contrast cell detection using multilevel classification.
International journal for numerical methods in biomedical engineering
Aug 23, 2017
In this paper, we propose a fully automated learning-based approach for detecting cells in time-lapse phase contrast images. The proposed system combines 2 machine learning approaches to achieve bottom-up image segmentation. We apply pixel-wise class...
Prospective identification of hematopoietic lineage choice by deep learning.
Nature methods
Feb 20, 2017
Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoi...
Artificial intelligence for optimal in vitro fertilization morphokinetics.
European journal of obstetrics, gynecology, and reproductive biology
Jul 1, 2025
OBJECTIVE: To create an artificial intelligence model able to determine the morphokinetic phases of an embryo by utilizing time-lapse imaging (TLI) videos.
Time will tell: time-lapse technology and artificial intelligence to set time cut-offs indicating embryo incompetence.
Human reproduction (Oxford, England)
Dec 1, 2024
STUDY QUESTION: Can more reliable time cut-offs of embryo developmental incompetence be generated by combining time-lapse technology (TLT), artificial intelligence, and preimplantation genetics screening for aneuploidy (PGT-A)?
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