Work

Intelligent System for Automated Spheroid Segmentation Using Machine Learning

Year: 2025

Type: book-chapter

Abstract: Image segmentation is a crucial task of medical image processing, including the analysis of multicellular tumour spheroids (MTSs), a common in vitro model used in cancer research for drug screening. Accurate segmentation of MTSs images allows the extraction of the morphological features necessary for the evaluation of the efficacy of the treatment they undergo. This paper presents an artificial intelligence (AI)-based segmentation system for the analysis of RGB images of MTS using machine learning (ML) classifiers. Unlike previous methods designed for high-performance microscope images, our system focuses on RGB images captured by standard bench-top optical microscopes, offering a cost-effective and accessible solution for research. The preliminary results demonstrate the efficacy of the ML approach in achieving the desired outcome.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250399

Authors: Alessandra Introvaia, Andrea Bezze, Sara Muccio, Clara Mattu, Gabriella Balestra

Institution: Politecnico di Torino

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: European Commission, NextGenerationEU