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Semantic redundancy-aware implicit neural compression for multidimensional biomedical image data.

Yifan Ma1, Chengqiang Yi1, Yao Zhou1

  • 1School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.

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This summary is machine-generated.

This study introduces Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS), an intelligent approach for compressing vast biomedical image data. SINCS significantly enhances compression efficiency and speed for diverse imaging dimensions while preserving high fidelity for downstream analysis.

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Area of Science:

  • Biomedical Imaging
  • Data Compression
  • Artificial Intelligence

Background:

  • Advanced imaging generates massive, multi-dimensional biomedical data, challenging traditional compression methods.
  • Conventional techniques struggle with storage, transmission, and sharing of complex image datasets.
  • Need for efficient compression that maintains data integrity for analysis.

Purpose of the Study:

  • To develop an intelligent image compression approach for arbitrary-dimensional biomedical data.
  • To leverage semantic redundancy in the implicit neural function domain for improved compression.
  • To enhance compression ratio, fidelity, and speed for biomedical image storage and transmission.

Main Methods:

  • Proposed Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS).
  • Utilized implicit neural functions to exploit semantic redundancy in biomedical data.
  • Incorporated saliency maps for guided compression.
  • Employed weight transfer and residual entropy coding for speed optimization.

Main Results:

  • Achieved over 2000-fold compression ratio on 2D, 2D-T, 3D, and 4D biomedical images.
  • Demonstrated significant improvements in compression efficiency and fidelity.
  • Showcased enhanced compression speed while maintaining high image quality.
  • Ensured reliable performance for downstream tasks like segmentation and quantitative analysis.

Conclusions:

  • SINCS offers a breakthrough in biomedical image compression, addressing challenges posed by advanced imaging techniques.
  • The approach effectively utilizes semantic redundancy for superior compression performance across various data dimensions.
  • SINCS enables efficient storage, transmission, and sharing of large-scale biomedical image data, facilitating high-efficiency downstream analyses.