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

arXiv: https://arxiv.org/abs/2101.02183 · HTML: https://mlsanity.com/paper/2101.02183/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2021.

## Built on by (most cited first, 6 of 6)

- [Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation](https://mlsanity.com/paper/2203.15015/) (2022)
- [Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data](https://mlsanity.com/paper/2110.04875/) (2021)
- [Code-free development and deployment of deep segmentation models for digital pathology](https://mlsanity.com/paper/2111.08430/) (2021)
- [Seeded iterative clustering for histology region identification](https://mlsanity.com/paper/2211.07425/) (2022)
- [Scribble-based fast weak-supervision and interactive corrections for segmenting whole slide images](https://mlsanity.com/paper/2402.08333/) (2024)
- [Leveraging Weak Supervision for Cell Localization in Digital Pathology Using Multitask Learning and Consistency Loss](https://mlsanity.com/paper/2412.15392/) (2024)

## Abstract (arXiv)

Image based biomarker discovery typically requires an accurate segmentation of histologic structures (e.g., cell nuclei, tubules, epithelial regions) in digital pathology Whole Slide Images (WSI). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized cohorts. Here, we present an open-source tool, Quick Annotator (QA), designed to improve annotation efficiency of histologic structures by orders of magnitude. While the user annotates regions of interest (ROI) via an intuitive web interface, a deep learning (DL) model is concurrently optimized using these annotations and applied to the ROI. The user iteratively reviews DL results to either (a) accept accurately annotated regions, or (b) correct erroneously segmented structures to improve subsequent model suggestions, before transitioning to other ROIs. We demonstrate the effectiveness of QA over comparable manual efforts via three use cases. These include annotating (a) 337,386 nuclei in 5 pancreatic WSIs, (b) 5,692 tubules in 10 colorectal WSIs, and (c) 14,187 regions of epithelium in 10 breast WSIs. Efficiency gains in terms of annotations per second of 102x, 9x, and 39x were respectively witnessed while retaining f-scores >.95, suggesting QA may be a valuable tool for efficiently fully annotating WSIs employed in downstream biomarker studies.