Automatic Delineation of Tumor Spheroids in Microscopic Images Using Deep-Learning

Tumor spheroid growth assays are used to evaluate the potential of cancer therapies in vitro. During such experiments, extensive microscopic image series are generated, which are commonly analyzed using threshold-based delineations. However, due to treatment-induced morphological changes of the spheroids, very time-consuming manual corrections are often required. The goal of our work was the development of an AI-based method for accurate and automated delineation of spheroid growth assays, ultimately reducing the reliance on manual delineation and corrections. Spheroids were grown from mouse pheochromocytoma (MPC) cells and subjected to irradiation with particle-emitting radioligands. Spheroid growth was monitored over 35 days. N = 38090 images, acquired within seven experiments and two studies, were included. Spheroids were delineated with a threshold-based method followed by manual corrections and the resulting delineations served as ground truth for network training and testing. The data were divided into two independent data sets: one for training and internal validation using a 5-fold cross-validation (N = 21567; main data set) and another for final independent testing (N = 16523). The network was developed using the nnU-Net v2 deep-learning (DL) framework. DL-based and manual delineations were compared using the Dice similarity coefficient (DSC). Additionally, treatment effects in a spheroid experiment were compared by quantifying half-maximum spheroid control doses (SCD₅₀). The median DSC values in the main and test data sets were 0.979 and 0.974, respectively. In the main data set, only 7% (N = 1571) of the DL-generated delineations and 8% (N = 1304) in the test data set showed DSC < 0.9, indicating high performance. The SCD₅₀ values were comparable between manual (day 13: 0.086 ± 0.001, day 35: 0.150 ± 0.001) and DL-based delineations (day 13: 0.083 ± 0.002, day 35: 0.149 ± 0.007). The network enables fast and accurate delineation of tumor spheroids in treatment response assays, reducing the time needed to delineate all spheroid images of a single experiment from several days with the previously applied method to a few hours only.