000108312 001__ 108312 000108312 005__ 20230519145351.0 000108312 0247_ $$2doi$$a10.1016/j.cmpb.2020.105837 000108312 0248_ $$2sideral$$a121154 000108312 037__ $$aART-2021-121154 000108312 041__ $$aeng 000108312 100__ $$aLacalle, D. 000108312 245__ $$aSpheroidJ: An Open-Source Set of Tools for Spheroid Segmentation 000108312 260__ $$c2021 000108312 5060_ $$aAccess copy available to the general public$$fUnrestricted 000108312 5203_ $$aBackground and objectives: Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumour behaviour, and for testing different preclinical and clinical treatments. In order to speed up the study of spheroids, imaging methods that automatically segment and measure spheroids are instrumental; and, several approaches for automatic segmentation of spheroid images exist in the literature. However, those methods fail to generalise to a diversity of experimental conditions. The aim of this work is the development of a set of tools for spheroid segmentation that works in a diversity of settings. Methods: In this work, we have tackled the spheroid segmentation task by first developing a generic segmentation algorithm that can be easily adapted to different scenarios. This generic algorithm has been employed to reduce the burden of annotating a dataset of images that, in turn, has been employed to train several deep learning architectures for semantic segmentation. Both our generic algorithm and the constructed deep learning models have been tested with several datasets of spheroid images where the spheroids were grown under several experimental conditions, and the images acquired using different equipment. Results: The developed generic algorithm can be particularised to different scenarios; however, those particular algorithms fail to generalise to different conditions. By contrast, the best deep learning model, constructed using the HRNet-Seg architecture, generalises properly to a diversity of scenarios. In order to facilitate the dissemination and use of our algorithms and models, we present SpheroidJ, a set of open-source tools for spheroid segmentation. Conclusions: In this work, we have developed an algorithm and trained several models for spheroid segmentation that can be employed with images acquired under different conditions. Thanks to this work, the analysis of spheroids acquired under different conditions will be more reliable and comparable; and, the developed tools will help to advance our understanding of tumour behaviour. 000108312 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/MTM2017-88804-P 000108312 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000108312 590__ $$a7.027$$b2021 000108312 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b20 / 112 = 0.179$$c2021$$dQ1$$eT1 000108312 591__ $$aMEDICAL INFORMATICS$$b6 / 31 = 0.194$$c2021$$dQ1$$eT1 000108312 591__ $$aENGINEERING, BIOMEDICAL$$b20 / 98 = 0.204$$c2021$$dQ1$$eT1 000108312 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b12 / 111 = 0.108$$c2021$$dQ1$$eT1 000108312 594__ $$a9.7$$b2021 000108312 592__ $$a1.329$$b2021 000108312 593__ $$aHealth Informatics$$c2021$$dQ1 000108312 593__ $$aComputer Science Applications$$c2021$$dQ1 000108312 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000108312 700__ $$aCastro-Abril, H.A. 000108312 700__ $$0(orcid)0000-0001-7232-7588$$aRandelovic, T. 000108312 700__ $$aDomínguez, C. 000108312 700__ $$aHeras, J. 000108312 700__ $$aMata, E. 000108312 700__ $$aMata, G. 000108312 700__ $$aMéndez, Y. 000108312 700__ $$aPascual, V. 000108312 700__ $$0(orcid)0000-0003-2410-5678$$aOchoa, I.$$uUniversidad de Zaragoza 000108312 7102_ $$11003$$2443$$aUniversidad de Zaragoza$$bDpto. Anatom.Histolog.Humanas$$cArea Histología 000108312 773__ $$g200, 105837 (2021), [7 pp.]$$pComput. methods programs biomed.$$tComputer Methods and Programs in Biomedicine$$x0169-2607 000108312 8564_ $$s303121$$uhttps://zaguan.unizar.es/record/108312/files/texto_completo.pdf$$yPostprint 000108312 8564_ $$s2465017$$uhttps://zaguan.unizar.es/record/108312/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000108312 909CO $$ooai:zaguan.unizar.es:108312$$particulos$$pdriver 000108312 951__ $$a2023-05-18-13:27:01 000108312 980__ $$aARTICLE