Quick Annotator: an open-source digital pathology based rapid image annotation tool

Miao, Runtian; Tóth, Róbert; Zhou, Yu; Madabhushi, Anant; Janowczyk, Andrew

doi:10.48550/arxiv.2101.02183

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…lf the user prefers to annotate locally, we have added options to ingest and export annotations in an Extensible Markup Language ( XML) 24 format readable by the commonly used WSI viewer Aperio ImageScope 25 . The authors note two complimentary works: HistomicsML 26 and Quick Annotator 27 , both use superpixels 28 and active learning 29 to speed the annotation process. HistomicsML also uses HistomicsUI for deployment, and Quick Annotator is run locally in the QuPath slide viewer 30 .…”

Section: Discussionmentioning

confidence: 99%

A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

Lutnick

Manthey

Becker

et al. 2021

Preprint

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Image-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power. We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides.

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