Grid structures are common in high-throughput assays to parallelize experiments in biochemical or biological experiments. Manual analysis of grid images is laborious, time-consuming, expensive, and critical in terms of reproducibility. However, it is still common to do such analysis manually, as there is no standardized software for automated analysis. In this paper, we introduce a generic method to automatically detect grid structures in images and to perform flexible spot-wise analysis after successful grid detection. The deep learning-based approach of the grid structure detection allows being flexible concerning different grid types. The combination with a robust parameter estimation algorithm lowers the requirements of the detection quality and thus enhances robustness. Further, the method conducts semi-automated grid detection if a fully automated processing fails. An open-source software tool Grid Screener that implements the proposed methods is provided as a ready-for-use tool for researchers. The usability is demonstrated by taking different criteria into account, which are important for a successful application. We present the benefits of our proposed tool Grid Screener utilizing three different grid types in the context of high-throughput screening to show our contribution towards further lab automation. Our tool performs much faster than manual analysis, while maintaining or even enhancing accuracy.

Cancer is a devastating disease and the second leading cause of death worldwide. However, the development of resistance to current therapies is making cancer treatment more difficult. Combining the multi-omics data of individual tumors with information on their in-vitro Drug Sensitivity and Resistance Test (DSRT) can help to determine the appropriate therapy for each patient. Miniaturized high-throughput technologies, such as the droplet microarray, enable personalized oncology. We are developing a platform that incorporates DSRT profiling workflows from minute amounts of cellular material and reagents. Experimental results often rely on image-based readout techniques, where images are often constructed in grid-like structures with heterogeneous image processing targets. However, manual image analysis is time-consuming, not reproducible, and impossible for high-throughput experiments due to the amount of data generated. Therefore, automated image processing solutions are an essential component of a screening platform for personalized oncology. We present our comprehensive concept that considers assisted image annotation, algorithms for image processing of grid-like high-throughput experiments, and enhanced learning processes. In addition, the concept includes the deployment of processing pipelines. Details of the computation and implementation are presented. In particular, we outline solutions for linking automated image processing for personalized oncology with high-performance computing. Finally, we demonstrate the advantages of our proposal, using image data from heterogeneous practical experiments and challenges.