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Enhanced Communication of Tumor Margins Using 3D Scanning and Mapping

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CAIMAN: an online algorithm repository for Cancer Image Analysis.

Constantino Carlos Reyes-Aldasoro¹, Michael K Griffiths, Deniz Savas

¹Cancer Research UK Tumour Microcirculation Group, Department of Oncology, The University of Sheffield, K Floor, School of Medicine, Dentistry and Health, UK. c.reyes@sheffield.ac.uk

Computer Methods and Programs in Biomedicine

|August 10, 2010

Summary

CAIMAN provides an accessible online platform for cancer research image analysis. Researchers can upload images for quantitative results without programming, aiding vascular biology studies.

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Life Sciences
Cancer Research
Vascular Biology Image Analysis

Background:

Cancer research generates complex imaging data.
Quantitative analysis of experimental images is crucial for biological insights.
Access to specialized image analysis tools can be a barrier for researchers.

Purpose of the Study:

To introduce CAIMAN (CAncer IMage ANalysis), an online algorithm repository.
To provide researchers with accessible tools for analyzing cancer research and life science images.
To facilitate quantitative image analysis, particularly in vascular biology, without requiring programming skills.

Main Methods:

Development of an online algorithm repository (CAIMAN).
User-friendly website for image uploading.
Automated image analysis with results returned via email.

Main Results:

CAIMAN offers a repository of algorithms for image analysis.
Researchers can obtain quantitative results from uploaded images.
The platform serves as a preliminary analysis tool, complementing sophisticated software.

Conclusions:

CAIMAN provides a valuable, user-friendly resource for quantitative image analysis in cancer research.
The platform democratizes access to image analysis for researchers lacking programming expertise.
CAIMAN serves as an effective first step for image analysis in life sciences and vascular biology.