Semantic redundancy-aware implicit neural compression for multidimensional biomedical image data.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
03 Sep 2024
03 Sep 2024
Historique:
received:
09
02
2024
accepted:
27
08
2024
medline:
4
9
2024
pubmed:
4
9
2024
entrez:
3
9
2024
Statut:
epublish
Résumé
The surge in advanced imaging techniques has generated vast biomedical image data with diverse dimensions in space, time and spectrum, posing big challenges to conventional compression techniques in image storage, transmission, and sharing. Here, we propose an intelligent image compression approach with the first-proved semantic redundancy of biomedical data in the implicit neural function domain. This Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS) can notably improve the compression efficiency for arbitrary-dimensional image data in terms of compression ratio and fidelity. Moreover, with weight transfer and residual entropy coding strategies, it shows improved compression speed while maintaining high quality. SINCS yields high quality compression with over 2000-fold compression ratio on 2D, 2D-T, 3D, 4D biomedical images of diverse targets ranging from single virus to entire human organs, and ensures reliable downstream tasks, such as object segmentation and quantitative analyses, to be conducted at high efficiency.
Identifiants
pubmed: 39227646
doi: 10.1038/s42003-024-06788-0
pii: 10.1038/s42003-024-06788-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1081Informations de copyright
© 2024. The Author(s).
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