ArticleEndocrinology2022
STAGETOOL, a Novel Automated Approach for Mouse Testis Histological Analysis.
Article in Endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Machine learning and automation methods for the segmentation, classification and quantification of testicular tissue sections.Reproduction & fertility · 2026Article
- Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model.Andrology · 2025Article
- SATINN v2: automated image analysis for mouse testis histology with multi-laboratory data integration†.Biology of reproduction · 2025Article
- ASB1 engages with ELOB to facilitate SQOR ubiquitination and HRedox biology · 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spermatogenesis is a complex differentiation process that takes place in the seminiferous tubules. A specific organization of spermatogenic cells within the seminiferous epithelium enables a synchronous progress of germ cells at certain steps of differentiation on the spermatogenic pathway. This can be observed in testis cross-sections where seminiferous tubules can be classified into distinct stages of constant cellular composition (12 stages in the mouse). For a detailed analysis of spermatogenesis, these stages have to be individually observed from testis cross-sections. However, the recognition of stages requires special training and expertise. Furthermore, the manual scoring is laborious considering the high number of tubule cross-sections that have to be analyzed. To facilitate the analysis of spermatogenesis, we have developed a convolutional deep neural network-based approach named "STAGETOOL." STAGETOOL analyses histological images of 4',6-diamidine-2'-phenylindole dihydrochloride (DAPI)-stained mouse testis cross-sections at ×400 magnification, and very accurately classifies tubule cross-sections into 5 stage classes and cells into 9 categories. STAGETOOL classification accuracy for stage classes of seminiferous tubules of a whole-testis cross-section is 99.1%. For cellular level analysis the F1 score for 9 seminiferous epithelial cell types ranges from 0.80 to 0.98. Furthermore, we show that STAGETOOL can be applied for the analysis of knockout mouse models with spermatogenic defects, as well as for automated profiling of protein expression patterns. STAGETOOL is the first fluorescent labeling-based automated method for mouse testis histological analysis that enables both stage and cell-type recognition. While STAGETOOL qualitatively parallels an experienced human histologist, it outperforms humans time-wise, therefore representing a major advancement in male reproductive biology research.
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.