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