Authors
Arpit Tandon, Ayse B Oktay, Eric McAfee, Adrian J Green, Brian E Howard, Mihir Shah, Ruchir R Shah
Published in
Microscopy (Oxford, England). Nov 13, 2026. Epub Nov 13, 2026.
Abstract
The increasing volume and heterogeneity of microscopy data in biomedical research present challenges for automated image analysis and the integration of assay-specific computational methods. We developed PixelVision, a modular, web-based framework that integrates deep learning models, quantitative image analysis, and expert review across diverse biological imaging modalities. The framework supports single-organism imaging, two- and three-dimensional cell culture analysis, whole-slide imaging, and interactive annotation through a shared infrastructure for model deployment, version management, and data processing. Quantitative evaluations demonstrated application-specific classification and segmentation performance, with inference throughput of approximately seven high-resolution images per second on a single GPU. In a prospective zebrafish screening study, iterative model refinement using expert-reviewed classifications was associated with an increase in macro-averaged F1 score from 0.867 to 0.967 across three screening rounds. PixelVision provides a framework for integrating automated image analysis and expert assessment across heterogeneous microscopy workflows.
PMID:
43021648
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.
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