Strategies for implementing hardware-assisted high-throughput cellular image analysis

Recent advances in imaging technology for biomedicine, including high-speed microscopy, automated microscopy, and imaging flow cytometry are poised to have a large impact on clinical diagnostics, drug discovery, and biological research. Enhanced acquisition speed, resolution, and automation of sample handling are enabling researchers to probe biological phenomena at an increasing rate and achieve intuitive image-based results. However, the rich image sets produced by these tools are massive, possessing potentially millions of frames with tremendous depth and complexity. As a result, the tools introduce immense computational requirements, and, more importantly, the fact that image analysis operates at a much lower speed than image acquisition limits its ability to play a role in critical tasks in biomedicine such as real-time decision making. In this work, we present our strategy for high-throughput image analysis on a graphical processing unit platform. We scrutinized our original algorithm for detecting, tracking, and analyzing cell morphology in high-speed images and identified inefficiencies in image filtering and potential shortcut routines in the morphological analysis stage. Using our "grid method" for image enhancements resulted in an 8.54× reduction in total run time, whereas origin centering allowed using a look up table for coordinate transformation, which reduced the total run time by 55.64×. Optimization of parallelization and implementation of specialized image processing hardware will ultimately enable real-time analysis of high-throughput image streams and bring wider adoption of assays based on new imaging technologies.