ArticleSoftwareX2025
HistomicsTK: A Python toolkit for pathology image analysis algorithms.
Article in SoftwareX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Image-based epigenetic profiling with deep learning and high-speed super-resolution microscopy.Epigenetics & chromatin · 2026Article
- Plant Microtechnique with Resin: Towards Plant Histolomics.Plants (Basel, Switzerland) · 2026Article
- Digital Pathology-Based Comparison of PyRadiomics and HistomicsTK for Nuclei Classification in Melanoma Whole Slide Images.International journal of biomedical imaging · 2026Article
- Deep learning finds convergent melanocytic morphology despite noisy archival slides.Cell reports methods · 2025Article
- Article
- Histolytics: A panoptic spatial analysis framework for interpretable histopathology.Computational and structural biotechnology journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Growth in the digital imaging of glass tissue slides has produced petabytes of data, however, this data remains underutilized in biomedical research due in part to a lack of open-source software. HistomicsTK is an open-source Python package that provides preprocessing, segmentation, and feature extraction capabilities for building histology image processing pipelines. HistomicsTK can function as a standalone Python package or serve containerized pipelines through a web-based interface using the Digital Slide Archive platform. This paper provides an overview of HistomicsTK with illustrative use cases and describes how this project engages the community in software development and maintenance.
Indexed as
Identifiers
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.