Lacalle David, Castro-Abril Héctor Alfonso, Randelovic Teodora, Domínguez César, Heras Jónathan, Mata Eloy, Mata Gadea, Méndez Yolanda, Pascual Vico, Ochoa Ignacio
Department of Mathematics and Computer Science, University of La Rioja, Spain.
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.
Comput Methods Programs Biomed. 2021 Mar;200:105837. doi: 10.1016/j.cmpb.2020.105837. Epub 2020 Nov 13.
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.
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.
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.
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.
球体是研究不同微环境特征对肿瘤行为的影响以及测试不同临床前和临床治疗方法时应用最广泛的三维模型。为了加速球体研究，能够自动分割和测量球体的成像方法至关重要；并且，文献中存在多种用于球体图像自动分割的方法。然而，这些方法无法推广到多种实验条件。这项工作的目的是开发一套适用于多种设置的球体分割工具。
在这项工作中，我们首先开发了一种通用分割算法来解决球体分割任务，该算法可轻松适应不同场景。此通用算法被用于减轻注释图像数据集的负担，而该数据集又被用于训练多个用于语义分割的深度学习架构。我们的通用算法和构建的深度学习模型都已在多个球体图像数据集上进行了测试，这些球体是在多种实验条件下生长的，并且图像是使用不同设备采集的。
所开发的通用算法可以针对不同场景进行特殊化处理；然而，那些特殊算法无法推广到不同条件。相比之下，使用HRNet - Seg架构构建的最佳深度学习模型能够很好地推广到多种场景。为了便于我们的算法和模型的传播与使用，我们展示了SpheroidJ，这是一套用于球体分割的开源工具。
在这项工作中，我们开发了一种算法并训练了多个用于球体分割的模型，这些模型可用于处理在不同条件下采集的图像。由于这项工作，对在不同条件下获取的球体的分析将更加可靠且具有可比性；并且，所开发的工具将有助于推进我们对肿瘤行为的理解。
