AIMC Topic: Cell Count
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Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.
Scientific reports
Jan 23, 2024
The availability of target cells expressing the HIV receptors CD4 and CCR5 in genital tissue is a critical determinant of HIV susceptibility during sexual transmission. Quantification of immune cells in genital tissue is therefore an important outcom...
Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva.
Scientific reports
Dec 21, 2023
Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surfac...
Indirect Sensing of Subclinical Intramammary Infections in Dairy Herds with a Milking Robot.
Sensors (Basel, Switzerland)
Nov 8, 2023
This study determined the impact of subclinical intramammary infections (IMIs), such as the major and minor udder pathogens (MaPs and MiPs), on the somatic cell count (SCC) in cow milk and investigated the possibilities of indirect sensing of the udd...
Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation.
Reproductive biomedicine online
Oct 2, 2023
RESEARCH QUESTION: Do cell numbers and degree of fragmentation in cleavage-stage embryos, assessed manually, correlate with evaluations made by deep learning algorithm model iDAScore v2.0?
Using optimal transport theory to optimize a deep convolutional neural network microscopic cell counting method.
Medical & biological engineering & computing
Aug 3, 2023
Medical image processing has become increasingly important in recent years, particularly in the field of microscopic cell imaging. However, accurately counting the number of cells in an image can be a challenging task due to the significant variation...
Deep learning-based classification of microalgae using light and scanning electron microscopy images.
Micron (Oxford, England : 1993)
Jun 28, 2023
Microalgae possess diverse applications, such as food production, animal feed, cosmetics, plastics manufacturing, and renewable energy sources. However, uncontrolled proliferation, known as algal bloom, can detrimentally impact ecosystems. Therefore,...
Mobile-CellNet: Automatic Segmentation of Corneal Endothelium Using an Efficient Hybrid Deep Learning Model.
Cornea
Dec 12, 2022
PURPOSE: The corneal endothelium, the innermost layer of the human cornea, exhibits a morphology of predominantly hexagonal cells. These endothelial cells are believed to have limited regeneration capacity, and their density decreases over time. Endo...
Deep learning characterization of brain tumours with diffusion weighted imaging.
Journal of theoretical biology
Nov 8, 2022
Glioblastoma multiforme (GBM) is one of the most deadly forms of cancer. Methods of characterizing these tumours are valuable for improving predictions of their progression and response to treatment. A mathematical model called the proliferation-inva...
Accurate detection of dairy cow mastitis with deep learning technology: a new and comprehensive detection method based on infrared thermal images.
Animal : an international journal of animal bioscience
Sep 29, 2022
Mastitis is one of the most common diseases in dairy cows and has a negative impact on their welfare and life, causing significant economic losses to the dairy industry. Many attempts have been made to develop a detection method for mastitis using th...
A disector-based framework for the automatic optical fractionator.
Journal of chemical neuroanatomy
Jul 15, 2022
Stereology-based methods provide the current state-of-the-art approaches for accurate quantification of numbers and other morphometric parameters of biological objects in stained tissue sections. The advent of artificial intelligence (AI)-based deep ...
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