title: Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields.
abstract: Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.
authors.affiliation: Department of Mathematics and Statistics, University of Turku, Turku, Finland.|Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland.|Université Grenoble-Alpes, F-38000 Grenoble, France.|CEA, iRTSV, Biologie à Grande Echelle, F-38054 Grenoble, France.|INSERM, U1038, F-38054 Grenoble, France.
authors.forename: Sean
authors.initials: S
authors.lastname: Robinson
authors.affiliation: Université Grenoble-Alpes, F-38000 Grenoble, France.|CEA, iRTSV, Biologie à Grande Echelle, F-38054 Grenoble, France.|INSERM, U1038, F-38054 Grenoble, France.
authors.forename: Laurent
authors.initials: L
authors.lastname: Guyon
authors.affiliation: Department of Mathematics and Statistics, University of Turku, Turku, Finland.|School of Health Sciences, University of Tampere, Tampere, Finland.
authors.forename: Jaakko
authors.initials: J
authors.lastname: Nevalainen
authors.affiliation: Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland.|Institute of Biomedicine, University of Turku, Turku, Finland.|Turku Centre for Biotechnology, University of Turku, Turku, Finland.
authors.forename: Mervi
authors.initials: M
authors.lastname: Toriseva
authors.affiliation: Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland.|Institute of Biomedicine, University of Turku, Turku, Finland.|Turku Centre for Biotechnology, University of Turku, Turku, Finland.
authors.forename: Malin
authors.initials: M
authors.lastname: Åkerfelt
authors.affiliation: Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland.|Institute of Biomedicine, University of Turku, Turku, Finland.|Turku Centre for Biotechnology, University of Turku, Turku, Finland.
authors.forename: Matthias
authors.initials: M
authors.lastname: Nees
mesh_terms: D045744:Cell Line, Tumor; D018920:Coculture Techniques; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D008297:Male; D008390:Markov Chains; D036641:Microscopy, Fluorescence, Multiphoton; D008954:Models, Biological; D011471:Prostatic Neoplasms / Q000473:pathology*