AIMC Topic: Time-Lapse Imaging

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Automatic grading of human blastocysts from time-lapse imaging.

Computers in biology and medicine
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
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
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
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
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...

ReSCU-Nets: Recurrent U-Nets for segmentation of three-dimensional microscopy data.

The Journal of cell biology
Segmenting multidimensional microscopy data requires high accuracy across many images (e.g., time points or Z slices) and is thus a labor-intensive part of biological image processing pipelines. We present ReSCU-Nets, recurrent convolutional neural n...

Artificial intelligence for optimal in vitro fertilization morphokinetics.

European journal of obstetrics, gynecology, and reproductive biology
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)
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)?

Time-Lapse Imaging and Artificial Intelligence: It is Just the End of the Beginning!

Journal of obstetrics and gynaecology Canada : JOGC = Journal d'obstetrique et gynecologie du Canada : JOGC

Imagerie time-lapse et intelligence artificielle : Ce n'est que la fin du début!

Journal of obstetrics and gynaecology Canada : JOGC = Journal d'obstetrique et gynecologie du Canada : JOGC