Lossy Image Compression in a Preclinical Multimodal Imaging Study.
The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compress...
| Published in: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1826 - 1851 |
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| Main Authors: | , , , , , , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169808795&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808795 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808795 162997537 165038349 169808795 169808795 10.1007/s10278-023-00800-5 169808795 ppf: 1826 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lossy Image Compression in a Preclinical Multimodal Imaging Study. aug: au: Cunha, Francisco F. Blüml, Valentin Zopf, Lydia M. Walter, Andreas Wagner, Michael Weninger, Wolfgang J. Thomaz, Lucas A. Tavora, Luís M. N. da Silva Cruz, Luis A. Faria, Sergio M. M. affil: Instituto de Telecomunicações, Morro do Lena—Alto do Vieiro, Leiria, Portugal sug: subj: Image Interpretation, Computer Assisted Tomography, X-Ray Computed Magnetic Resonance Imaging Neoplasms Diagnosis Human Funding Source Observer Bias ab: The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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