Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study

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dc.authoridAlis, Deniz/0000-0002-7045-1793
dc.authoridArslan, Aydan/0000-0002-6073-1927
dc.authoridSeker, Mustafa Ege/0000-0001-7664-5786
dc.authoridSirolu, Sabri/0000-0002-8443-0838
dc.authoridoksuz, ilkay/0000-0001-6478-0534
dc.contributor.authorAlis, Deniz
dc.contributor.authorKartal, Mustafa Said
dc.contributor.authorSeker, Mustafa Ege
dc.contributor.authorGuroz, Batuhan
dc.contributor.authorBasar, Yeliz
dc.contributor.authorArslan, Aydan
dc.contributor.authorSirolu, Sabri
dc.date.accessioned2024-10-26T18:11:18Z
dc.date.available2024-10-26T18:11:18Z
dc.date.issued2023
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractBackground: Although systems such as Prostate Imaging Quality (PI-QUAL) have been proposed for quality assessment, visual evaluations by human readers remain somewhat inconsistent, particularly among lessexperienced readers.Objectives: To assess the feasibility of deep learning (DL) for the automated assessment of image quality in biparametric MRI scans and compare its performance to that of less-experienced readers.Methods: We used bi-parametric prostate MRI scans from the PI-CAI dataset in this study. A 3-point Likert scale, consisting of poor, moderate, and excellent, was utilized for assessing image quality. Three expert readers established the ground-truth labels for the development (500) and testing sets (100). We trained a 3D DL model on the development set using probabilistic prostate masks and an ordinal loss function. Four less-experienced readers scored the testing set for performance comparison.Results: The kappa scores between the DL model and the expert consensus for T2W images and ADC maps were 0.42 and 0.61, representing moderate and good levels of agreement. The kappa scores between the lessexperienced readers and the expert consensus for T2W images and ADC maps ranged from 0.39 to 0.56 (fair to moderate) and from 0.39 to 0.62 (fair to good).Conclusions: Deep learning (DL) can offer performance comparable to that of less-experienced readers when assessing image quality in bi-parametric prostate MRI, making it a viable option for an automated quality assessment tool. We suggest that DL models trained on more representative datasets, annotated by a larger group of experts, could yield reliable image quality assessment and potentially substitute or assist visual evaluations by human readers.
dc.identifier.doi10.1016/j.ejrad.2023.110924
dc.identifier.issn0720-048X
dc.identifier.issn1872-7727
dc.identifier.pmid37354768
dc.identifier.scopus2-s2.0-85162969433
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ejrad.2023.110924
dc.identifier.urihttps://hdl.handle.net/20.500.12418/30601
dc.identifier.volume165
dc.identifier.wosWOS:001027705700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Ireland Ltd
dc.relation.ispartofEuropean Journal of Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Learning
dc.subjectProstate
dc.subjectMRI
dc.subjectImage quality
dc.titleDeep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study
dc.typeArticle

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