AIMC Topic: Staining and Labeling

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Deep learning-based transformation of H&E stained tissues into special stains.

Nature communications Aug 12, 2021
Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstra...
Reference Standards Biopsy, Large-Core Needle Pathology, Clinical Kidney Diseases Diagnosis, Differential Coloring Agents Algorithms Reproducibility of Results Sensitivity and Specificity Staining and Labeling Kidney Diagnosis, Computer-Assisted Deep Learning Humans
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Automated identification of glomeruli and synchronised review of special stains in renal biopsies by machine learning and slide registration: a cross-institutional study.

Histopathology Aug 3, 2021
AIMS: Machine learning in digital pathology can improve efficiency and accuracy via prescreening with automated feature identification. Studies using uniform histological material have shown promise. Generalised application requires validation on sli...
Humans Biopsy Kidney Glomerulus Coloring Agents Pathology, Clinical Neural Networks, Computer Image Processing, Computer-Assisted Staining and Labeling
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Label-free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis.

Cytometry. Part A : the journal of the International Society for Analytical Cytology Jun 19, 2021
The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will con...
Humans Staining and Labeling Adenocarcinoma Neural Networks, Computer Algorithms Deep Learning
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DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set.

Scientific data May 20, 2021
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistently demonstrated to correlate with prognosis. We present a morphologic a...
Tissue Array Analysis Cell Nucleus Eosine Yellowish-(YS) Hematoxylin Humans Deep Learning Staining and Labeling Prognosis Lymphoma, Large B-Cell, Diffuse
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Learning deep features for dead and living breast cancer cell classification without staining.

Scientific reports May 13, 2021
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer ce...
Humans Neural Networks, Computer Cell Line, Tumor Staining and Labeling Automation Female Breast Neoplasms Deep Learning
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Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging.

Scientific reports Mar 24, 2021
Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to ...
Molecular Imaging Fluorescent Antibody Technique Gene Expression Flow Cytometry Mesenchymal Stem Cells Image Processing, Computer-Assisted Staining and Labeling Computational Biology Biomarkers Gene Expression Profiling Artificial Intelligence
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An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

Journal of neuroscience methods Feb 17, 2021
BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields of pathology, toxicology, cancer and behavior. As part of a program to develop accurate, precise an...
Deep Learning Image Processing, Computer-Assisted Staining and Labeling Coloring Agents Humans Algorithms Reproducibility of Results
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A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jan 28, 2021
Reliable counting of glomeruli and evaluation of glomerulosclerosis in renal specimens are essential steps to assess morphological changes in kidney and identify individuals requiring treatment. Because microscopic identification of sclerosed glomeru...
Deep Learning Staining and Labeling Humans Kidney Eosine Yellowish-(YS) Hematoxylin
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In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.

Cytometry. Part A : the journal of the International Society for Analytical Cytology Dec 13, 2020
To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational ...
Machine Learning Staining and Labeling Kidney Supervised Machine Learning Humans Neural Networks, Computer
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A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Dec 9, 2020
Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tis...
Methyl Green Image Interpretation, Computer-Assisted Eosine Yellowish-(YS) Hematoxylin Child Staining and Labeling Liver Cirrhosis Middle Aged Severity of Illness Index Adolescent Non-alcoholic Fatty Liver Disease Coloring Agents Microscopy Azo Compounds Liver Predictive Value of Tests Biopsy Female Reproducibility of Results Decision Support Techniques Aged Retrospective Studies Humans Clinical Decision-Making Neural Networks, Computer Aged, 80 and over Software Adult Young Adult Male
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