FUSION NAVIGATOR

Functional Unit State Identification for WSI INTEGRATING MOLECULAR AND STRUCTURAL OMICS FOR BIOLOGICAL DISCOVERY AND PRECISION MEDICINE

AI-driven visualization and quantification of spatial OMICS

WHAT IS FUSION

FUSION is an interactive interface to view spatial transcriptomics data integrated with histopathology. This is driven by artificial intelligence (AI). AI segments Functional Tissue Units (FTU) and links these with gene expression data that informs on major and minor healthy and injured cell types directly mapped on histological features on the FTUs in the WSI.

HOW FUSION WORKS

State of the art machine learning algorithms in a user-friendly data input interface

Highly interactive frontend design to drive hypothesis development and testing

Cloud-hosted data storage made possible through Digital Slide Archive (DSA)

We want people to test FUSION

Bring your own data!

FUSION TOOLS

FUSION-Tools is a modular toolkit designed for developers to create custom visualization and analysis dashboards for high-resolution microscopy images. It brings key FUSION functionality to locally stored whole slide images (WSIs), offering a powerful way to explore your data.

Get started with FUSION-Tools and explore its capabilities!

FUSION Jupyterhub

To streamline user experience and broaden accessibility, Fusion-tools now includes a fully integrated JupyterHub environment as part of its open-source offering. This pre-configured deployment enables users—ranging from students and researchers to data scientists—to instantly create and run notebooks with full Fusion-tools functionality, without any manual setup.

By embedding JupyterHub directly into the fusion-tools ecosystem, we provide a modular, interactive platform for visualizing and analyzing spatial omics and histology data. This integrated environment not only simplifies onboarding but also accelerates exploratory workflows in spatial biology, making fusion-tools more accessible across diverse computational settings.

As part of our pilot initiative, we’ve launched a dedicated JupyterHub instance to demonstrate this seamless experience and gather feedback from the community. This effort lays the groundwork for broader adoption in institutional and cloud-based JupyterHub deployments.Get started with JupyterHub and explore its capabilities!

KEY Developers

ASSISTANT SCIENTIST

Anindya Sankar Paul

Expertise: Deep Learning, Federated Learning, Secure and Privacy Preserving AI, Signal Processing, Medical Imaging

PRINCIPAL INVESTIGATOR

Pinaki Sarder

Nicholas Lucarelli	PH.D. STUDENT, BIOMEDICAL ENGINEERING
Jessica Kirwan	Project Manager
Suraj Ramadugu	Image Analysis User Support
Suhas Katari Chaluva Kumar	System Analyst
Ujwala Guttikonda	Data Management Analyst
Sumanth Devarasetty	Web Developer
Praveen Kumar Dande	System Analyst
Nikhil Yerra	Data Management Analyst
Haitham Magdy	Data Management Analyst
Annika Holmstrom	Usability Coordinator

rest of the UFL HIVE Team

INDIANA UNIVERSITY

Michael Eadon

Data Generation

washington university

Sanjay Jain

MPI and Data Generation

KITWARE INC

David Manthey

Software and Production

HARVARD University

Lettie McGuire

Research Designer

Laura Barisoni	Usability
Jessica Ray	Usability
Yulia A. L. Strekalova	PEDP Lead
Tarek Ashkar	Data Generation
Seth Winfree	Analytics

contact details

Contact Sam Border <samuel.border@medicine.ufl.edu> and Pinaki Sarder <pinaki.sarder@ufl.edu> with queries, request for testing, and suggestions for enhancing FUSION.