  Multicellular tumor spheroids (MCTS) are advanced cell culture systems for
assessing the impact of combinatorial radio(chemo)therapy. They exhibit
therapeutically relevant in-vivo-like characteristics from 3D cell-cell and
cell-matrix interactions to radial pathophysiological gradients related to
proliferative activity and nutrient/oxygen supply, altering cellular
radioresponse. State-of-the-art assays quantify long-term curative endpoints
based on collected brightfield image time series from large treated spheroid
populations per irradiation dose and treatment arm. Here, spheroid control
probabilities are documented analogous to in-vivo tumor control probabilities
based on Kaplan-Meier curves. This analyses require laborious spheroid
segmentation of up to 100.000 images per treatment arm to extract relevant
structural information from the images, e.g., diameter, area, volume and
circularity. While several image analysis algorithms are available for spheroid
segmentation, they all focus on compact MCTS with clearly distinguishable outer
rim throughout growth. However, treated MCTS may partly be detached and
destroyed and are usually obscured by dead cell debris. We successfully train
two Fully Convolutional Networks, UNet and HRNet, and optimize their
hyperparameters to develop an automatic segmentation for both untreated and
treated MCTS. We systematically validate the automatic segmentation on larger,
independent data sets of spheroids derived from two human head-and-neck cancer
cell lines. We find an excellent overlap between manual and automatic
segmentation for most images, quantified by Jaccard indices at around 90%. For
images with smaller overlap of the segmentations, we demonstrate that this
error is comparable to the variations across segmentations from different
biological experts, suggesting that these images represent biologically unclear
or ambiguous cases.
