dc.contributor.author
Aldoj, Nader
dc.contributor.author
Biavati, Federico
dc.contributor.author
Dewey, Marc
dc.contributor.author
Hennemuth, Anja
dc.contributor.author
Asbach, Patrick
dc.contributor.author
Sack, Ingolf
dc.date.accessioned
2024-03-01T14:19:46Z
dc.date.available
2024-03-01T14:19:46Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/42605
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-42329
dc.description.abstract
Magnetic resonance elastography (MRE) for measuring viscoelasticity heavily depends on proper tissue segmentation, especially in heterogeneous organs such as the prostate. Using trained network-based image segmentation, we investigated if MRE data suffice to extract anatomical and viscoelastic information for automatic tabulation of zonal mechanical properties of the prostate. Overall, 40 patients with benign prostatic hyperplasia (BPH) or prostate cancer (PCa) were examined with three magnetic resonance imaging (MRI) sequences: T2-weighted MRI (T2w), diffusion-weighted imaging (DWI), and MRE-based tomoelastography, yielding six independent sets of imaging data per patient (T2w, DWI, apparent diffusion coefficient, MRE magnitude, shear wave speed, and loss angle maps). Combinations of these data were used to train Dense U-nets with manually segmented masks of the entire prostate gland (PG), central zone (CZ), and peripheral zone (PZ) in 30 patients and to validate them in 10 patients. Dice score (DS), sensitivity, specificity, and Hausdorff distance were determined. We found that segmentation based on MRE magnitude maps alone (DS, PG: 0.93 +/- 0.04, CZ: 0.95 +/- 0.03, PZ: 0.77 +/- 0.05) was more accurate than magnitude maps combined with T2w and DWI_b (DS, PG: 0.91 +/- 0.04, CZ: 0.91 +/- 0.06, PZ: 0.63 +/- 0.16) or T2w alone (DS, PG: 0.92 +/- 0.03, CZ: 0.91 +/- 0.04, PZ: 0.65 +/- 0.08). Automatically tabulated MRE values were not different from ground-truth values (P>0.05). In conclusion, MRE combined with Dense U-net segmentation allows tabulation of quantitative imaging markers without manual analysis and independent of other MRI sequences and can thus contribute to PCa detection and classification.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
prostate zones
en
dc.subject
Magnetic resonance elastography (MRE)
en
dc.subject
Dense U-net segmentation
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
2001
dcterms.bibliographicCitation.doi
10.1038/s41598-022-05878-5
dcterms.bibliographicCitation.journaltitle
Scientific Reports
dcterms.bibliographicCitation.number
1
dcterms.bibliographicCitation.originalpublishername
Springer Nature
dcterms.bibliographicCitation.volume
12
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.funding
Springer Nature DEAL
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
35132102
dcterms.isPartOf.eissn
2045-2322