David Lacalle1, Héctor Alfonso Castro-Abril2, Teodora Randelovic3, César Domínguez1, Jónathan Heras4, Eloy Mata1, Gadea Mata5, Yolanda Méndez1, Vico Pascual1, Ignacio Ochoa6. 1. Department of Mathematics and Computer Science, University of La Rioja, Spain. 2. Tissue MicroEnvironment (TME) lab, Institute for Health Research Aragón (IIS Aragón), Zaragoza, Spain; Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain; Grupo de modelado y métodos numéricos en Ingeniería, Universidad Nacional de Colombia, Colombia. 3. Tissue MicroEnvironment (TME) lab, Institute for Health Research Aragón (IIS Aragón), Zaragoza, Spain; Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain. 4. Department of Mathematics and Computer Science, University of La Rioja, Spain. Electronic address: jonathan.heras@unirioja.es. 5. Confocal Microscopy Core Unit, Spanish National Cancer Research Centre, Madrid, Spain. 6. Tissue MicroEnvironment (TME) lab, Institute for Health Research Aragón (IIS Aragón), Zaragoza, Spain; Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain; Biomedical Research Networking Centre in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
Abstract
BACKGROUND 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.
BACKGROUND 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.
Authors: Alena Arlova; Chengcheng Jin; Abigail Wong-Rolle; Eric S Chen; Curtis Lisle; G Thomas Brown; Nathan Lay; Peter L Choyke; Baris Turkbey; Stephanie Harmon; Chen Zhao Journal: J Pathol Inform Date: 2022-01-20