[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123402-en":3,"doc-seo-123402-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123402,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Intelligent System for Automated Spheroid Segmentation Using Machine Learning","Image segmentation is central to medical image processing, especially for analyzing multicellular tumour spheroids (MTSs), a widely used in vitro cancer research model for drug screening. Precise segmentation enables reliable extraction of morphological features needed to judge treatment efficacy. This work proposes an AI-driven segmentation system using machine learning classifiers to process RGB MTS images captured with standard bench-top optical microscopes, targeting a cost-effective alternative to methods requiring high-performance microscopy. Preliminary results confirm the ML approach’s effectiveness.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nIntelligent System for Automated Spheroid Segmentation Using Machine Learning  \nOriginal  \nIntelligent System for Automated Spheroid Segmentation Using Machine Learning / Introvaia, Alessandra; Bezze, Andrea; Muccio, Sara; Mattu, Clara; Balestra, Gabriella. -ELETTRONICO. -327:(2025), pp. 557-561. (Intervento presentato al convegno 35th Medical Informatics Europe Conference-MIE 2025 tenutosi a Glasgow (UK) nel 19–21 May 2025) [10 .3233/shti250399] .  \nAvailability:  \nThis version is available at: 11583/3002338 since: 2025-08-06T07:23:21Z  \nPublisher: IOS Press  \nPublished  \nDOI:10.3233/shti250399  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n04 October 2025  \nIntelligent Health Systems – From Technology to Data and Knowledge  \nE. Andrikopoulou et al. (Eds.)© 2025 The Authors.  \nThis article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/SHTI250399  \n557  \nIntelligent System for Automated Spheroid Segmentation Using Machine Learning  \nAlessandra INTROVAIAa,1, Andrea BEZZEb, Sara MUCCIOb, Clara MATTUb, and Gabriella BALESTRAa  \na Department of Electronics and Telecommunications – Politecnico di Torino, Italy b Department of Mechanical and Aerospace Engineering – Politecnico di Torino, Italy ORCiD ID: Alessandra Introvaia [https://orcid.org/0009-0003-6526-7697](https://orcid.org/0009-0003-6526-7697)[ ](https://orcid.org/0009-0003-6526-7697)ORCiD ID: Andrea Bezze [https://orcid.org/0000-0002-8061-5034](https://orcid.org/0000-0002-8061-5034)  \nORCiD ID: Sara Muccio [https://orcid.org/0009-0002-1757-7597](https://orcid.org/0009-0002-1757-7597)  \nORCiD ID: Clara Mattu [https://orcid.org/0000-0002-3969-8771](https://orcid.org/0000-0002-3969-8771)[ ](https://orcid.org/0000-0002-3969-8771)ORCiD ID: Gabriella Balestra [https://orcid.org/0000-0003-2717-648X](https://orcid.org/0000-0003-2717-648X)  \nAbstract. 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 costeffective and accessible solution for research. The preliminary results demonstrate  \nthe efficacy of the ML approach in achieving the desired outcome.  \nKeywords. Multicellular spheroids, In vitro models, Image segmentation, Artificial  \nIntelligence, Machine Learning  \n1. Introduction  \nThe success rate of translating nanomedicines from experimental research to clinical trials is less than 30%[1]. Consequently, preclinical validation systems must ensure rapid and reliable screening for high-throughput evaluation. Multicellular tumour spheroids (MTSs) are widely used in vitro models for preclinical drug screening in cancer research [2] . These three-dimensional, non-adherent cell aggregates typically consist of heterogeneous cell populations and can be tailored to replicate the physiological spatial structure of tumours like glioblastoma multiforme (GBM) . This flexibility makes them valuable for studying tumour biology and drug screening. Nevertheless, colorimetric viability tests, commonly used to assess drug effects on MTSs, are expensive, timeconsuming, and destructive, prompting the need for alternatives. In this c","cbCaipNoqrOeW3i0","https://ap.wps.com/l/cbCaipNoqrOeW3i0","pdf",569652,1,6,"English","en",105,"# Introduction\n## Preclinical screening needs\n## Role of optical microscopy and segmentation\n## Related automated and deep learning methods\n# Methods\n## Dataset","[{\"question\":\"Why is automated segmentation important for multicellular tumour spheroids (MTSs)?\",\"answer\":\"Accurate segmentation supports extraction of morphological features linked to treatment outcomes, enabling fast and reliable preclinical screening. It also reduces the time-consuming, operator-dependent variability of manual analysis.\"},{\"question\":\"What problem does this paper address compared with previous segmentation approaches?\",\"answer\":\"Earlier high-performing methods often target expensive microscope images. The proposed system focuses on RGB images acquired using standard bench-top optical microscopes to improve accessibility and reduce cost.\"},{\"question\":\"How does the proposed system approach MTS image segmentation?\",\"answer\":\"It uses AI-based machine learning classifiers to segment RGB images of MTSs. The preliminary results indicate that this ML approach achieves the desired segmentation outcome.\"}]","Intelligent System for Automated Spheroid Segmentation Using Machine Learning | PDF",1785816299,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"intelligent-system-for-automated-spheroid-segmentation-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/intelligent-system-for-automated-spheroid-segmentation-using-machine-learning/123402/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is automated segmentation important for multicellular tumour spheroids (MTSs)?","Question",{"text":76,"@type":77},"Accurate segmentation supports extraction of morphological features linked to treatment outcomes, enabling fast and reliable preclinical screening. It also reduces the time-consuming, operator-dependent variability of manual analysis.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What problem does this paper address compared with previous segmentation approaches?",{"text":81,"@type":77},"Earlier high-performing methods often target expensive microscope images. The proposed system focuses on RGB images acquired using standard bench-top optical microscopes to improve accessibility and reduce cost.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed system approach MTS image segmentation?",{"text":85,"@type":77},"It uses AI-based machine learning classifiers to segment RGB images of MTSs. 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