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Baikalov, A. ; Wang, E.* ; Neill, D.* ; Shetty, N.* ; Waldrop, T.* ; Liu, K.* ; Delahoussaye, A.* ; Aguilar, E.* ; Mims, N.* ; Bartzsch, S. ; Schüler, E.*

A fully automated workflow for the digital image analysis of the intestinal microcolony survival assay.

Sci. Rep. 16:9633 (2026)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Manual analysis of histopathological images is often time-consuming and painstaking, and it is also prone to errors arising from subjective evaluation criteria and human mistakes. To address these issues, we created a fully automated workflow (AW) to enumerate jejunal crypts in a microcolony survival assay to quantify gastrointestinal damage from radiation. After obtaining images of jejunal slices from irradiated mice, the AW performed cropping and normalizing of the individual slice images for resolution and color; using deep learning to detect crypts on each slice; using a tailored algorithm to enumerate the crypts; and tabulating and saving the results. A graphical user interface was developed to allow users to review and correct the automated results. Manual counting of crypts exhibited a mean absolute percent deviation of (34 +/- 26)% between individuals vs. the group mean across counters, which was reduced to (11 +/- 6)% across the 3 most-experienced counters. AW counts deviated from experts' mean counts by (10 +/- 8)%. With a single button press, the AW processed a sample image dataset from 60 mice in a few hours. The AW thereby allowed rapid, automated evaluation of the microcolony survival assay with accuracy comparable to that of trained experts and without subjective inter-observer variation.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Ai ; Automation ; Deep Learning ; Histopathology ; Image Analysis ; Microcolony Survival Assay; Cellularity Following Irradiation; Crypt Survival; Cells; Radiation
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Zeitschrift Scientific Reports
Quellenangaben Band: 16, Heft: 1, Seiten: , Artikelnummer: 9633 Supplement: ,
Verlag Springer
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Radiation Medicine (IRM)
Förderungen Cancer Prevention and Research Institute of Texas
National Cancer Institute of the National Institutes of Health
UTHealth Houston Center for Clinical and Translational Sciences Program
Klinikum rechts der Isar of the Technical University of Munich
University Cancer Foundation via the Institutional Research Grant program at MD Anderson Cancer Center