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.
Original language | English |
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Article number | e143798 |
Journal | PLoS ONE |
Volume | 10 |
Issue number | 12 |
DOIs | |
Publication status | Published - 2015 |
MoE publication type | A1 Journal article-refereed |