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arXiv · 2503.18037

Compression benchmarking of holotomography data using the OME-Zarr storage format

Dohyeon Lee·Juyeon Park·Juheon Lee·Chungha Lee·YongKeun Park
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Abstract

Holotomography (HT) is a label-free, three-dimensional imaging technique that captures refractive index distributions of biological samples at sub-micron resolution. As modern HT systems enable high-throughput and large-scale acquisition, they produce terabyte-scale datasets that require efficient data management. This study presents a systematic benchmarking of data compression strategies for HT data stored in the OME-Zarr format, a cloud-compatible, chunked data structure suitable for scalable imaging workflows. Using representative datasets-including embryo, tissue, and birefringent tissue volumes-we evaluated combinations of preprocessing filters and 25 compression configurations across multiple compression levels. Performance was assessed in terms of compression ratio, bandwidth, and decompression speed. A throughput-based evaluation metric was introduced to simulate real-world conditions under varying network constraints, supporting optimal compressor selection based on system bandwidth. The results offer practical guidance for storage and transmission of large HT datasets and serve as a reference for implementing scalable, FAIR-aligned imaging workflows in cloud and high-performance computing environments.

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Dohyeon Lee, Juyeon Park, Juheon Lee, Chungha Lee, YongKeun Park. 2025-03-23. Compression benchmarking of holotomography data using the OME-Zarr storage format. https://arxiv.org/abs/2503.18037

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