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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Imaging Studies III: Computed Tomography

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Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Related Experiment Video

Updated: Jul 10, 2026

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography
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Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Published on: August 12, 2025

Compression benchmarking of holotomography data using OME-Zarr format.

Dohyeon Lee1,2, Juyeon Park1,2, Juheon Lee1,2

  • 1Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.

Plos One
|July 8, 2026
PubMed
Summary
This summary is machine-generated.

Holotomography (HT) data compression is essential for large datasets. Pcodec, Blosc-zstd, and zstd offer efficient strategies for storing and transmitting HT imaging data, depending on bandwidth.

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Published on: October 2, 2021

Area of Science:

  • Biomedical imaging
  • Data science
  • Computational biology

Background:

  • Holotomography (HT) is a label-free 3D imaging technique for refractive index mapping.
  • High-throughput HT generates terabyte-scale datasets requiring efficient management.
  • OME-Zarr is a cloud-compatible format for scalable imaging data.

Purpose of the Study:

  • To benchmark data compression strategies for holotomography datasets.
  • To evaluate compression performance under varying network conditions.
  • To provide guidance for scalable, FAIR-aligned imaging workflows.

Main Methods:

  • Systematic benchmarking of 13 compression algorithms and preprocessing filters.
  • Evaluation using six representative HT datasets from diverse biological samples.
  • Assessment of compression ratio, bandwidth, decompression speed, and a novel throughput metric.

Main Results:

  • Optimal compression strategy is bandwidth-dependent.
  • Pcodec demonstrated the most balanced performance across various bandwidths.
  • Blosc-zstd and zstd also showed strong performance.

Conclusions:

  • Compression significantly impacts HT data storage and transmission efficiency.
  • Pcodec is recommended for balanced performance in HT data management.
  • Findings support scalable, cloud-based imaging workflows.