Metric-driven Pre-acquisition Diagnostics for Automated Microscopy Ultraviolet Surface Excitation (MUSE)
Abstract
Background Automated microscopy enables large-scale tiled imaging but remains sensitive to variations in alignment, illumination, focus, and sample coverage that may only be detected after acquisition. We present an adaptive pre-acquisition quality-control framework for microscopy ultraviolet surface excitation (MUSE) that uses sparse scout imaging and tile-level metrics to guide acquisition and selective rescanning. Methods The framework quantifies brightness, dynamic range, focus, tissue fraction, and exposure error across the field of view. For example, in a representative 10 x 10 acquisition (100 tiles, 20-megapixel imagery), the mean p99 brightness was 0.525 (CV 10.4%), the mean dynamic range was 0.331, the median focus score was 0.0333, and the median tissue fraction was 0.943. Furthermore, applying a conservative exposure-error threshold (|error| > 0.25) identified 25% of tiles as rescan candidates. Results These results show that compact tile-level metrics can be converted into actionable quality gates, enabling early detection of local acquisition errors and targeted rescanning to improve mosaic consistency while minimizing unnecessary acquisition.
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BibTeX
@misc{nunez2026metricdriven,
title = {{Metric-driven Pre-acquisition Diagnostics for Automated Microscopy Ultraviolet Surface Excitation (MUSE)}},
author = {M. Nunez and B. Davies},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.22.760904},
url = {https://www.biorxiv.org/content/10.64898/2026.10.22.760904v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Metric-driven Pre-acquisition Diagnostics for Automated Microscopy Ultraviolet Surface Excitation (MUSE)
AU - M. Nunez
AU - B. Davies
PY - 2026
DA - 2026/10/22
PB - bioRxiv
DO - 10.64898/2026.10.22.760904
UR - https://www.biorxiv.org/content/10.64898/2026.10.22.760904v1
AB - Background Automated microscopy enables large-scale tiled imaging but remains sensitive to variations in alignment, illumination, focus, and sample coverage that may only be detected after acquisition. We present an adaptive pre-acquisition quality-control framework for microscopy ultraviolet surface excitation (MUSE) that uses sparse scout imaging and tile-level metrics to guide acquisition and selective rescanning. Methods The framework quantifies brightness, dynamic range, focus, tissue fraction, and exposure error across the field of view. For example, in a representative 10 x 10 acquisition (100 tiles, 20-megapixel imagery), the mean p99 brightness was 0.525 (CV 10.4%), the mean dynamic range was 0.331, the median focus score was 0.0333, and the median tissue fraction was 0.943. Furthermore, applying a conservative exposure-error threshold (|error| > 0.25) identified 25% of tiles as rescan candidates. Results These results show that compact tile-level metrics can be converted into actionable quality gates, enabling early detection of local acquisition errors and targeted rescanning to improve mosaic consistency while minimizing unnecessary acquisition.
ER -