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Related Experiment Video

Updated: May 23, 2025

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

Published on: July 8, 2014

Intelligent System for Automated Spheroid Segmentation Using Machine Learning.

Alessandra Introvaia¹, Andrea Bezze², Sara Muccio²

¹Department of Electronics and Telecommunications - Politecnico di Torino, Italy.

Studies in Health Technology and Informatics

|May 17, 2025

Summary

This summary is machine-generated.

This study introduces an AI-powered system for segmenting multicellular tumour spheroids (MTSs) from standard microscope images. The machine learning approach accurately analyzes MTS images for cancer drug screening research.

Keywords:

Artificial Intelligence Image segmentation In vitro models Machine Learning Multicellular spheroids

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High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

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Published on: March 7, 2017

Area of Science:

Medical image processing
Cancer research
In vitro modelling

Background:

Accurate segmentation of multicellular tumour spheroids (MTSs) is vital for cancer research and drug screening.
Existing methods often require high-performance microscopes, limiting accessibility.
Morphological feature extraction from MTS images aids in evaluating treatment efficacy.

Purpose of the Study:

To develop an artificial intelligence (AI)-based image segmentation system for multicellular tumour spheroids (MTSs).
To enable analysis of RGB images from standard optical microscopes, enhancing accessibility and cost-effectiveness.
To utilize machine learning (ML) classifiers for accurate MTS segmentation.

Main Methods:

Development of an AI-based system utilizing machine learning (ML) classifiers.
Focus on segmenting RGB images captured by standard bench-top optical microscopes.
Application of the system to analyze multicellular tumour spheroids (MTSs).

Main Results:

The AI system demonstrates efficacy in segmenting MTS images.
Preliminary results indicate successful application of ML classifiers for this task.
The system provides a cost-effective and accessible solution for MTS analysis.

Conclusions:

The developed AI-based segmentation system is effective for analyzing MTS images.
Machine learning offers a viable approach for accessible medical image segmentation in cancer research.
This method supports morphological analysis for drug screening and treatment evaluation.