ReviewThe American journal of pathology2021
Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.
Review in The American journal of pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed.
- Data-driven thresholds for standardized classification of severe Alzheimer's disease neuropathology using digital neuropathology.Brain pathology (Zurich, Switzerland) · 2026Article
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Enhanced Burn Wound Healing and Conversion Prevention Through Inhibition of High Mobility Group Box 1 in a Scald Burn Rat Model.Journal of burn care & research : official publication of the American Burn Association · 2026Article
- Radiomic Analysis as a Powerful Tool for Cytological Images of Benign Thyroid Nodules Treated by Thermal Radiofrequency Ablation.Bioengineering (Basel, Switzerland) · 2026Article
- TAM Pathway Receptor Proteins: Differential Expression in Healthy Human Masticatory Mucosa.Journal of periodontal research · 2025Article
- Classification of Trypanosoma brucei mammalian life cycle stages using Deep Learning Algorithms.PLoS neglected tropical diseases · 2025Article
- Multi scale supervised entropy weighted binary pattern for texture classification.Scientific reports · 2025Article
- Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies.Communications biology · 2025Article
- Artificial Intelligence-Enhanced Analysis of Genomic DNA Visualized with Nanoparticle-Tagged Peptides under Electron Microscopy.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Advancing Hydrogel-Based 3D Cell Culture Systems: Histological Image Analysis and AI-Driven Filament Characterization.Biomedicines · 2025Article
- Towards H&E Referenced Multiplex Immunofluorescence Interpretation: Spatial Co-localization, Cell Feature Validation, and Virtual H&E Generation.Research square · 2025Article
- AI-Assisted Cell Culture System.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Advancements in pathology: Digital transformation, precision medicine, and beyond.Journal of pathology informatics · 2025Review
- Cutting-edge AI tools revolutionizing scientific research in life sciences.Biotechnologia · 2025Review
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation.eLife · 2024Article
- Artificial intelligence applications for immunology laboratory: image analysis and classification study of IIF photos.Immunologic research · 2024Article
- [A review on depth perception techniques in organoid images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.Dento maxillo facial radiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from radiology and pathology to cancer biology and immunology. Specifically, a growth in research on deep learning has led to the widespread application of computer-visualization techniques for analyzing and mining data from biomedical images. The availability of open-source software packages and the development of novel, trainable deep neural network architectures has led to increased accuracy in cell detection and segmentation algorithms. By automating cell segmentation, it is now possible to mine quantifiable cellular and spatio-cellular features from microscopy images, providing insight into the organization of cells in various pathologies. This mini-review provides an overview of the current state of the art in deep learning- and artificial intelligence-based methods of segmentation and data mining of cells in microscopy images of tissue.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.