ArticleSensors (Basel, Switzerland)2021
Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 17 citations in OpenAlex.
- Label-free candidate dOCM biomarkers associated with blastocyst quality using total cell count as a proxy.Biomedical optics express · 2026Article
- Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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Abstract
backgroundCell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time.
methodsMulti-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image.
resultsThe experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques-Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization.
conclusionComparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.
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