AIMC Topic: Cell Count

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A machine-learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes.

Biotechnology and bioengineering
The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and control strategies to produce safe and consistent products for patients. Raman spectroscopy is a ubiq...

A new method for automatic counting of ovarian follicles on whole slide histological images based on convolutional neural network.

Computers in biology and medicine
The ovary is a complex endocrine organ that shows significant structural and functional changes in the female reproductive system over recurrent cycles. There are different types of follicles in the ovarian tissue. The reproductive potential of each ...

Equivalent circuit models for a biomembrane impedance sensor and analysis of electrochemical impedance spectra based on support vector regression.

Medical & biological engineering & computing
In this study, an electrochemical impedance biosensor was developed as a simple and fast method for real-time monitoring of biofilm binding properties via continuous impedance spectroscopy. To prepare the sensing membrane, cells were immobilized onto...

Automated segmentation of the corneal endothelium in a large set of 'real-world' specular microscopy images using the U-Net architecture.

Scientific reports
Monitoring the density of corneal endothelial cells (CEC) is essential in the management of corneal diseases. Its manual calculation is time consuming and prone to errors. U-Net, a neural network for biomedical image segmentation, has shown promising...

Light-driven ultrasensitive self-powered cytosensing of circulating tumor cells via integration of biofuel cells and a photoelectrochemical strategy.

Journal of materials chemistry. B
Herein, a light-driven, membrane-less and mediator-less self-powered cytosensing platform via integration of biofuel cells (BFCs) and a photoelectrochemical strategy was developed for ultrasensitive detection of circulating tumor cells (CTCs). To con...

Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex.

Journal of chemical neuroanatomy
Collection of unbiased stereology data currently relies on relatively simple, low throughput technology developed in the mid-1990s. In an effort to improve the accuracy and efficiency of these integrated hardware-software-digital microscopy systems, ...

Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology.

Journal of chemical neuroanatomy
In recent decades stereology-based studies have played a significant role in understanding brain aging and developing novel drug discovery strategies for treatment of neurological disease and mental illness. A major obstacle to further progress in a ...

Estimation of somatic cell count levels of hard cheeses using physicochemical composition and artificial neural networks.

Journal of dairy science
This study addresses the prediction of the somatic cell counts of the milk used in the production of sheep cheese using artificial neural networks. To achieve this objective, the neural network was designed using 33 parameters of the physicochemical ...

Case study: Evaluating quarter and composite milk sampling for detection of subclinical intramammary infections in dairy cattle.

Preventive veterinary medicine
Our objective was to evaluate a 200,000 cells/mL somatic cell count (SCC) cut-point on both the quarter and composite level to determine its effectiveness at identifying subclinical mastitis infections in one commercial dairy herd in Central New York...

U-Net: deep learning for cell counting, detection, and morphometry.

Nature methods
U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their dat...