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Related Experiment Video

Updated: May 22, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Fusion Annotator: A Platform for Accelerating Consensus-Driven Ground Truth Generation with AI Assistance.

Suhas K C Kumar¹, Fatemeh Afsari¹, Nicholas Lucarelli¹

¹Division of Nephrology, Hypertension, and Renal Transplantation-Quantitative Health Section, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL.

Proceedings of Spie--The International Society for Optical Engineering

|May 21, 2026

Summary

Fusion Annotator accelerates AI development in renal pathology by enabling expert-driven, consensus-based digital annotation. This cloud-based platform standardizes dataset creation, overcoming limitations of traditional tools for computational pathology.

Keywords:

AI-assisted annotation Cloud-based annotation platform Digital pathology annotation Expert consensus datasets Machine learning for histopathology Renal pathology

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Last Updated: May 22, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Area of Science:

Computational pathology
Digital pathology
Artificial intelligence in medicine

Background:

AI advancements in pathology require high-quality annotated datasets for model development.
Renal pathology faces challenges in ground-truth generation due to complex workflows and variability.
Limited data accessibility and expertise hinder robust AI model creation in renal pathology.

Purpose of the Study:

To introduce Fusion Annotator, a novel cloud-first digital annotation platform.
To address the need for standardized, expert-driven dataset creation in renal pathology.
To accelerate the development of artificial intelligence models for computational renal pathology.

Main Methods:

Fusion Annotator is a web-based platform facilitating collaborative, consensus-oriented annotation.
It integrates with AI models to pre-identify regions of interest, reducing manual effort.
The platform supports customizable schemas, reference guidance, and multi-expert input for quality assurance.

Main Results:

Fusion Annotator enables secure, multi-institutional access and centralized data management.
It streamlines the creation of expert-annotated datasets for specific renal disease cohorts.
The platform facilitates consensus building and iterative refinement through pathologist feedback.

Conclusions:

Fusion Annotator overcomes limitations of traditional annotation tools for AI in renal pathology.
It provides a scalable solution for generating high-quality datasets essential for AI development.
The platform supports diverse renal pathology cohorts, advancing computational pathology research.