A robust quality estimation method for medical image segmentation with small datasets

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dc.authorid0000-0003-4057-7851
dc.contributor.authorUslu, Fatmatulzehra
dc.contributor.authorVarela, Marta
dc.date.accessioned2026-02-08T15:15:09Z
dc.date.available2026-02-08T15:15:09Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe quality check of automatic image analysis results is a necessity to eliminate poor outcomes. There are few work on segmentation quality estimation when there is no reference mask available. Generative -based and regression approaches generally have strict assumptions on shape/contrast of anatomical structures, which may fail when there are abnormalities in images or in the presence of domain shift. Ensemble approach promises more generalisability to various types of images; however, they are costly to train. Also, none of these methods were designed for small datasets. To address these shortcomings, this paper presents a segmentation quality estimation method for small datasets with arbitrary dimensions, which is validated with 2 D, 3 D and 4 D image datasets with roughly 20 training images, and on the segmentation of retinal vessel and left atrium segmentation. Although our method uses an ensemble for image segmentation, its design reduces its parameter size. We describe possible scenarios relating the amount of agreement across the base models' outputs in an ensemble to quality scores; then, present a technique to deal with high quality score estimation for poor segmentation as a result of the base models to largely agree on mistakes. We assess the performance of our method in the presence of different sources of domain shift, and compare it with methods selected from the aforementioned approaches. We found robust quality score estimation, generalisable to different datasets. Our code would be available upon acceptance.
dc.identifier.doi10.1016/j.bspc.2024.106300
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85191661388
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2024.106300
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5632
dc.identifier.volume95
dc.identifier.wosWOS:001237560900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectImage segmentation
dc.subjectEnsemble models
dc.subjectReliability
dc.subjectTrustworthiness
dc.subjectPearson correlation
dc.titleA robust quality estimation method for medical image segmentation with small datasets
dc.typeArticle

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