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.

翻译：多细胞肿瘤球体（MCTS）是评估联合放射（化学）疗法影响的高级细胞培养系统。它们展现出具有治疗相关性的类体内特征，包括三维细胞间及细胞-基质相互作用，以及与增殖活性和营养/氧气供应相关的径向病理生理梯度，这些特征会改变细胞的放射反应。最先进的检测方法基于每个照射剂量和治疗组所采集的大规模处理球体群体的明场图像时间序列，量化长期治愈终点。在此，球体控制概率以类似于基于卡普兰-迈耶曲线的体内肿瘤控制概率的方式被记录。该分析需要对每个治疗组多达10万张图像进行繁琐的球体分割，以从图像中提取相关结构信息，如直径、面积、体积和圆形度。虽然已有多种图像分析算法可用于球体分割，但它们均专注于在整个生长过程中具有清晰可辨外缘的致密MCTS。然而，经处理的MCTS可能部分脱落并损毁，且通常被死细胞碎片所遮蔽。我们成功训练了两种全卷积网络——UNet和HRNet，并通过优化其超参数，开发了一种适用于未经处理与经处理MCTS的自动分割方法。我们在源自两种人类头颈癌细胞系的更大规模独立球体数据集上系统验证了该自动分割方法。我们发现大多数图像的手动分割与自动分割具有极佳的重合度，其杰卡德指数约90%。对于分割重合度较低的图像，我们证明该误差与不同生物学专家分割结果间的变异相当，表明这些图像代表了生物学上不明确或模糊的案例。