ArticleMethodsX2025
Segmentation and quantification of testicular histology images using machine learning bioimage analysis tools; Ilastik and Fiji software.
Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histomorphological and histochemical techniques are widely used in infertility studies to assess testicular damage, determine the mechanisms involved, investigate potential interventions strategies, monitor treatment response and prognosis. Testis, a primary male reproductive organ is a compartmentalized organ made up of several seminiferous tubules and supporting tissue. Hence, focal damage is common, and accordingly, making accurate and insightful deductions require careful analysis of almost the entire testis section. However, manual analysis of testis histology sections to extract quantifiable data is hectic, time-consuming, liable to bias and undetected patchy damages and inter-personal variability. To circumvent these challenges, we present a step-by-step workflow using free, open-source interactive machine learning-based bioimage analysis tools; ilastik and Fiji. Ilastik uses a random forest classifier to compute generic pixel or object features for image segmentation. The segmented images exported from ilastik are subsequently quantified in FIJI to extract data for statistical analysis.•A step-by-step workflow using free, open-source interactive machine learning-based bioimage analysis tools; ilastik and Fiji.•A semiautomated, reproducible, time saving, unbiased, and broad scope method for analysis and heterogeneous tissue images.•Extraction of quantifiable data from images for statistical analysis to make comprehensive conclusions.
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.