Image segmentation of treated and untreated tumor spheroids by Fully Convolutional Networks

TL;DR

This paper develops and validates deep learning models, UNet and HRNet, for automatic segmentation of tumor spheroids in brightfield images, including challenging cases of treated and damaged spheroids, achieving high accuracy comparable to expert annotations.

Contribution

The study introduces a robust deep learning-based segmentation method tailored for both untreated and treated tumor spheroids, addressing limitations of existing algorithms for damaged or obscured spheroids.

Findings

Achieved around 90% Jaccard index in segmentation accuracy.

Automatic segmentation performance is comparable to expert manual annotations.

Validated on large, independent datasets from two cancer cell lines.

Abstract

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.