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dc.contributor.authorTomasetti, Luca
dc.contributor.authorHansen, Stine
dc.contributor.authorKhanmohammadi, Mahdieh
dc.contributor.authorEngan, Kjersti
dc.contributor.authorHøllesli, Liv Jorunn
dc.contributor.authorKurz, Kathinka Dæhli
dc.contributor.authorKampffmeyer, Michael Christian
dc.date.accessioned2024-01-29T10:16:21Z
dc.date.available2024-01-29T10:16:21Z
dc.date.issued2023-09-01
dc.description.abstractPrecise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest mortality rate. While numerous deep neural network approaches have recently been proposed to tackle this problem, these methods require large amounts of annotated regions during training, which can be impractical in the medical domain where annotated data is scarce. As a remedy, we present a prototypical few-shot segmentation approach for ischemic lesion segmentation using only one annotated sample during training. The proposed approach leverages a novel self-supervised training mechanism that is tailored to the task of ischemic stroke lesion segmentation by exploiting color-coded parametric maps generated from Computed Tomography Perfusion scans. We illustrate the benefits of our proposed training mechanism, leading to considerable improvements in performance in the few-shot setting. Given a single annotated patient, an average Dice score of 0.58 is achieved for the segmentation of ischemic lesions.en_US
dc.identifier.citationTomasetti, Hansen, Khanmohammadi, Engan, Høllesli, Kurz, Kampffmeyer. Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation. IEEE International Symposium on Biomedical Imaging. 2023en_US
dc.identifier.cristinIDFRIDAID 2185857
dc.identifier.doi10.1109/ISBI53787.2023.10230655
dc.identifier.issn1945-7928
dc.identifier.issn1945-8452
dc.identifier.urihttps://hdl.handle.net/10037/32754
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE International Symposium on Biomedical Imaging
dc.relation.projectIDNorges forskningsråd: 315029en_US
dc.relation.projectIDNorges forskningsråd: 309439en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.titleSelf-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentationen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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