ArticleJournal of toxicologic pathology2019
Quantification of histopathological findings using a novel image analysis platform.
Article in Journal of toxicologic pathology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 46 citations in OpenAlex.
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- Comparison of automated and manual approaches for microglial quantification and classification: A focus on the HALO digital pathology platform.Brain pathology (Zurich, Switzerland) · 2026Article
- Comparison of QuPath and HALO Platforms for Analysis of the Tumor Microenvironment in Prostate Cancer.Laboratory investigation; a journal of technical methods and pathology · 2025Article
- Integrative Transcriptomic-Histological Analysis in Dilated Cardiomyopathy Unveils FGFR1 Inhibition as Anti-Cardiac Fibrotic and Cardioprotective Therapy.JACC. Basic to translational science · 2025Article
- Computer vision to predict cell seeding coverage in re-endothelialized mouse lungs.Scientific reports · 2025Article
- Report of the AI Pathology Task Force, Non-clinical Evaluation Expert Committee, Japan Pharmaceutical Manufacturers Association: an overview of recent publications about AI pathology.Journal of toxicologic pathology · 2025Review
- Exploring predictive biomarkers of efficacy and survival with nivolumab treatment for unresectable/recurrent esophageal squamous cell carcinoma.Esophagus : official journal of the Japan Esophageal Society · 2025Article
- Spatially resolved proteomics surveys the chemo-refractory proteins related to high-grade serous ovarian cancer.Clinical and translational medicine · 2025Article
- The Evolving Landscape of GEP-NENs in the Era of Precision Oncology: Molecular Insights into Tumor Heterogeneity.Cancers · 2025Review
- Interactions between Ploidy and Resource Availability Shape Clonal Evolution in Glioblastoma.Cancer research · 2025Article
- A novel method for histopathological analysis of oral squamous cell carcinoma: a pilot study using three-dimensional imaging to enhance local control.Frontiers in oncology · 2025Article
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- Comprehensive pan-cancer analysis of mitochondrial outer membrane permeabilisation activity reveals positive immunomodulation and assists in identifying potential therapeutic targets for immunotherapy resistance.Clinical and translational medicine · 2024Article
- Combination of pathological, biochemical and behavioral evaluations for peripheral neurotoxicity assessment in isoniazid-treated rats.Journal of toxicologic pathology · 2024Article
- CD64 plays a key role in diabetic wound healing.Frontiers in immunology · 2024Article
- Electrocardiographic Markers of Adverse Left Ventricular Remodeling and Myocardial Fibrosis in Severe Aortic Stenosis.Journal of clinical medicine · 2023Article
- A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies.Toxicological research · 2023Article
- Age-related morphometrics of normal adrenal glands based on deep learning-aided segmentation.Heliyon · 2023Article
- Targeting XBP1 mRNA splicing sensitizes glioblastoma to chemotherapy.FASEB bioAdvances · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital pathology, including image analysis and automatic diagnosis of pathological tissue, has been developed remarkably. HALO is an image analysis platform specialized for the study of pathological tissues, which enables tissue segmentation by using artificial intelligence. In this study, we used HALO to quantify various histopathological changes and findings that were difficult to analyze using conventional image processing software. Using the tissue classifier module, the morphological features of degeneration/necrosis of the hepatocytes and muscle fibers, bile duct in the liver, basophilic tubules and hyaline casts in the kidney, cortex in the thymus, and red pulp, white pulp, and marginal zone in the spleen were learned and separated, and areas of interest were quantified. Furthermore, using the cytonuclear module and vacuole module in combination with the tissue classifier module, the number of erythroblasts in the red pulp of the spleen and each area of acinar cells in the parotid gland were quantified. The results of quantitative analysis were correlated with the histopathological grades evaluated by pathologists. By using artificial intelligence and other functions of HALO, we recognized morphological features, analyzed histopathological changes, and quantified the histopathological grades of various findings. The analysis of histopathological changes using HALO is expected to support pathology evaluations.
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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.