BackgroundManual analysis of histopathological images is often not only time-consuming and painstaking but also prone to error from subjective evaluation criteria and human error. To address these issues, we created a fully automated workflow to enumerate jejunal crypts in a microcolony survival assay to quantify gastrointestinal damage from radiation.Methods and MaterialsAfter abdominal irradiation of mice, jejuna were obtained and prepared on histopathologic slides, and crypts were counted manually by trained individuals. The automated workflow (AW) involved obtaining images of jejunal slices from the irradiated mice, followed by cropping and normalizing the individual slice images for resolution and color; using deep learning-based semantic image segmentation to detect crypts on each slice; using a tailored algorithm to enumerate the crypts; and tabulating and saving the results. A graphical user interface (GUI) was developed to allow users to review and correct the automated results.ResultsCrypts counted manually 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. The AW processed a sample image dataset from 60 mice in a few hours and required only a few minutes of active user effort. AW counts deviated from experts’ mean counts by (10 ± 8)%. 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.HighlightsWe fully automated the digital image analysis of a microcolony survival assayAnalyzing 540 images takes a few hours with only minutes of active user effortThe automated workflow (AW) is just as accurate as trained expertsThe AW eliminates subjective inter-observer variation and human errorHuman review possible with built-in graphical user interfaceGraphical Abstract
