AIMC Topic: Cell Count

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A novel retinal ganglion cell quantification tool based on deep learning.

Scientific reports Jan 12, 2021
Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the dev...
Glaucoma Software Mice, Inbred C57BL Cell Count Retinal Ganglion Cells Animals Deep Learning Mice
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Pseudo-nuclear staining of cells by deep learning improves the accuracy of automated cell counting in a label-free cellular population.

Journal of bioscience and bioengineering Oct 17, 2020
Deep learning has emerged as a breakthrough tool for the segmentation of images without supporting human experts. Here, we propose an automated approach that uses deep learning to generate pseudo-nuclear staining of cells from phase contrast images. ...
Humans Deep Learning Staining and Labeling Cell Count Microscopy Automation
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Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer.

EBioMedicine Oct 8, 2020
BACKGROUND: An artificial intelligence method could accelerate the clinical implementation of tumour-stroma ratio (TSR), which has prognostic relevance in colorectal cancer (CRC). We, therefore, developed a deep learning model for the fully automated...
Humans Artificial Intelligence Prognosis Proportional Hazards Models Cell Count Stromal Cells Immunohistochemistry Tumor Microenvironment Male Middle Aged Cancer-Associated Fibroblasts Neoplasm Staging Neoplasm Grading Aged Adult Colorectal Neoplasms Retrospective Studies Female
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ALICE: a hybrid AI paradigm with enhanced connectivity and cybersecurity for a serendipitous encounter with circulating hybrid cells.

Theranostics Sep 2, 2020
A fully automated and accurate assay of rare cell phenotypes in densely-packed fluorescently-labeled liquid biopsy images remains elusive. Employing a hybrid artificial intelligence (AI) paradigm that combines traditional rule-based morphological ma...
Computer Security Internet of Things Liquid Biopsy Humans Machine Learning Software Female Pancreatic Neoplasms Predictive Value of Tests Principal Component Analysis Male Neoplastic Cells, Circulating Reproducibility of Results Middle Aged Cell Count Aged Image Processing, Computer-Assisted Microscopy, Fluorescence Biomarkers, Tumor
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Deep Learning for Assessing the Corneal Endothelium from Specular Microscopy Images up to 1 Year after Ultrathin-DSAEK Surgery.

Translational vision science & technology Aug 21, 2020
PURPOSE: To present a fully automatic method to estimate the corneal endothelium parameters from specular microscopy images and to use it to study a one-year follow-up after ultrathin Descemet stripping automated endothelial keratoplasty.
Cell Count Microscopy Endothelium, Corneal Reproducibility of Results Deep Learning
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CellCountCV-A Web-Application for Accurate Cell Counting and Automated Batch Processing of Microscopic Images Using Fully Convolutional Neural Networks.

Sensors (Basel, Switzerland) Jun 29, 2020
In vitro cellular models are promising tools for studying normal and pathological conditions. One of their important applications is the development of genetically engineered biosensor systems to investigate, in real time, the processes occurring in ...
Image Processing, Computer-Assisted Cell Count Microscopy Automation Humans Neural Networks, Computer
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Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.

Laboratory investigation; a journal of technical methods and pathology Sep 30, 2019
Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable a...
Cell Count Bone Marrow Cells Datasets as Topic Pathology Humans Machine Learning
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Automated Counting of Cancer Cells by Ensembling Deep Features.

Cells Sep 2, 2019
High-content and high-throughput digital microscopes have generated large image sets in biological experiments and clinical practice. Automatic image analysis techniques, such as cell counting, are in high demand. Here, cell counting was treated as a...
Cell Count Tumor Cells, Cultured Neoplasms Deep Learning Image Processing, Computer-Assisted Humans
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A machine-learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes.

Biotechnology and bioengineering Jul 21, 2019
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...
Bioreactors Cell Count Models, Biological CHO Cells Cricetulus Humans Cell Culture Techniques Machine Learning Animals Spectrum Analysis, Raman
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A new method for automatic counting of ovarian follicles on whole slide histological images based on convolutional neural network.

Computers in biology and medicine Jul 9, 2019
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 ...
Cell Count Animals Neural Networks, Computer Imaging, Three-Dimensional Ovarian Follicle Female Image Processing, Computer-Assisted
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