Variability in visual segmentation of digitized prostate tissue microarray cores.

Abstract

Objective: To examine bias associated with human-interactive semi-automated systems key components with machine vision used in quantitative histometry.

Study design: A standard image set of 20 images was created using 5 nuclei sampled from hematoxylin-eosin-stained sections of benign tissue within a prostate tissue microarray that were rotated through the cardinal directions. Four trained technicians performed segmentation of these images at the start, then at the end, of 3 daily sessions, creating a total analytic set of 480 observations. Measurements of nuclear area (NA), nuclear roundness factor (NRF), and mean optical density (MOD) were compared by segmenter, time, and rotational orientation.

Results: NA varied significantly among sessions (p < 0.0009) and session variance differed within segmenter (p < 0.0001). NRF was significant among segmenters (p < 0.001) and sessions (p < 0.0001), and in session (p < 0.0001) and intra-session differences (p = 0.026). Differences in MOD varied among sessions (p < 0.0001) and within sessions (p < 0.049).

Conclusion: Imaging systems remain vulnerable to statistical inter-segmenter variation, in spite of extensive efforts to eliminate variation among individual segmenters. As statistical significance often guides decision-making in morphometric analysis, statistically significant effects potentially produce bias. Current practices and quality assurance methods require review to eliminate individual operator effects in semiautomated machine systems.