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dc.contributor.authorSmistad, Erik
dc.contributor.authorØstvik, Andreas
dc.contributor.authorLøvstakken, Lasse
dc.date.accessioned2022-02-15T12:42:27Z
dc.date.available2022-02-15T12:42:27Z
dc.date.created2021-11-24T14:52:30Z
dc.date.issued2021
dc.identifier.issn1948-5719
dc.identifier.urihttps://hdl.handle.net/11250/2979098
dc.description.abstractThe use of deep learning and other machine learning techniques requires large amounts of annotated image data. There exist several tools to annotate images, however to our knowledge there are no tools made specifically for medical ultrasound (US) images. As US is a temporal imaging modality it is essential for such a tool to handle image sequences/videos. To make annotation of US images as easy and efficient as possible, we made a web-based tool called Annotation Web (AW) enabling image classification, segmentation, bounding box and landmark annotation of ultrasound videos. We have now open-sourced this tool, making it available to everyone for free, and we invite the community to use it 1 1 https://github.com/smistad/annotationweb/.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleAnnotation Web - An open-source web-based annotation tool for ultrasound imagesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.source.journalProceedings - IEEE Ultrasonics Symposiumen_US
dc.identifier.doi10.1109/IUS52206.2021.9593336
dc.identifier.cristin1958498
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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