Abstract Autonomous migration is central to neutrophil function and diverse disease processes. ComplexEye, our recently introduced multi-lens array microscope, enables high-throughput live-cell video acquisition for routine quantification of autonomous motility. However, such platforms generate data at extreme scale, creating substantial challenges for storage and transmission. Here we present FlowRoI, a fast, training-free framework for region-of-interest (RoI) extraction and RoI-aware compression in immune cell migration studies. FlowRoI computes optical flow between consecutive frames and derives RoI masks that effectively capture migrating cells in the evaluated dataset. Each frame and its RoI mask are then jointly encoded using JPEG2000 to achieve efficient, cell-focused compression. FlowRoI is computationally lightweight, operating at approximately 30 frames per second on a laptop with an Intel i7-1255U CPU—comparable to standard JPEG2000. At matched peak signal-to-noise ratio, FlowRoI achieves approximately 2 × higher compression rates than standard JPEG2000 in the evaluated dataset while preserving higher image quality in cellular regions. To assess downstream impact, we evaluated cell instance segmentation as a representative task. At comparable segmentation accuracy in our experiments, FlowRoI provides approximately 2 × higher compression efficiency. FlowRoI requires only a few hyperparameters and shows stable performance across the tested settings, facilitating practical parameter selection. Together, these findings demonstrate the feasibility of FlowRoI as a computationally efficient, domain-oriented approach for task-aware compression in bright-field neutrophil migration imaging. Its applicability to other cell types, imaging modalities, and experimental settings remains to be systematically evaluated.
Xu et al. (Sat,) studied this question.