dc.contributor.author | Jha, Debesh | |
dc.contributor.author | Pia H, Smedsrud | |
dc.contributor.author | Riegler, Michael | |
dc.contributor.author | Halvorsen, Pål | |
dc.contributor.author | de Lange, Thomas | |
dc.contributor.author | Johansen, Dag | |
dc.contributor.author | Johansen, Håvard D. | |
dc.date.accessioned | 2020-01-19T18:52:29Z | |
dc.date.accessioned | 2020-04-07T16:29:33Z | |
dc.date.available | 2020-01-19T18:52:29Z | |
dc.date.available | 2020-04-07T16:29:33Z | |
dc.date.issued | 2019-12-24 | |
dc.identifier.citation | Jha D, Pia H, Riegler M, Halvorsen P, de Lange T, Johansen D, Johansen HJ. Kvasir-SEG: A Segmented Polyp Dataset. Lecture Notes in Computer Science (LNCS). 2020;11962:451-462 | en |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | https://hdl.handle.net/10642/8391 | |
dc.description.abstract | Pixel-wise image segmentation is a highly demanding task
in medical-image analysis. In practice, it is difficult to find annotated
medical images with corresponding segmentation masks. In this paper,
we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp
images and corresponding segmentation masks, manually annotated by
a medical doctor and then verified by an experienced gastroenterologist.
Moreover, we also generated the bounding boxes of the polyp regions with
the help of segmentation masks. We demonstrate the use of our dataset
with a traditional segmentation approach and a modern deep-learning
based Convolutional Neural Network (CNN) approach. The dataset will
be of value for researchers to reproduce results and compare methods.
By adding segmentation masks to the Kvasir dataset, which only provide
frame-wise annotations, we enable multimedia and computer vision re-
searchers to contribute in the field of polyp segmentation and automatic
analysis of colonoscopy images. | en |
dc.description.sponsorship | This work is funded in part by the Research Council of Norway projects number 263248 (Privaton). We performed all computations in this paper on equipment provided by the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under contract 270053. | en |
dc.language.iso | en | en |
dc.publisher | Springer | en |
dc.relation.ispartof | MMM 2020: MultiMedia Modeling - 26th International Conference, MMM 2020, Daejeon, South Korea, January 5–8, 2020, Proceedings, Part II | |
dc.relation.ispartofseries | Lecture Notes in Computer Science;Volume 11962 | |
dc.rights | This is a post-peer-review, pre-copyedit version of a book chapter published in MultiMedia Modeling - 26th International Conference, MMM 2020, Daejeon, South Korea, January 5–8, 2020, Proceedings, Part II, that is part of the Lecture Notes in Computer Science book series (LNCS, volume 11962). The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-37734-2_37 | en |
dc.subject | Medical images | en |
dc.subject | Polyp segmentations | en |
dc.subject | Semantic segmentations | en |
dc.subject | Kvasir-SEG datasets | en |
dc.subject | Fuzzy c-mean clusters | en |
dc.subject | Deep Residual UNet architecture | en |
dc.title | Kvasir-SEG: A Segmented Polyp Dataset | en |
dc.type | Conference object | en |
dc.date.updated | 2020-01-19T18:52:29Z | |
dc.description.version | acceptedVersion | en |
dc.identifier.doi | http://dx.doi.org/10.1007/978-3-030-37734-2_37 | |
dc.identifier.cristin | 1776857 | |
dc.source.journal | Lecture Notes in Computer Science (LNCS) | |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551 | |
dc.subject.nsi | VDP::Technology: 500::Information and communication technology: 550::Computer technology: 551 | |
dc.subject.nsi | VDP::Medisinske fag: 700::Klinisk medisinske fag: 750::Gasteroenterologi: 773 | |
dc.subject.nsi | VDP::Midical sciences: 700::Clinical medical sciences: 750::Gastroenterology: 773 | |
dc.subject.nsi | VDP::Matematikk og naturvitenskap: 400 | |
dc.subject.nsi | VDP::Mathematics and natural scienses: 400 | |
dc.subject.nsi | VDP::Matematikk og naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering, visualisering, signalbehandling, bildeanalyse: 429 | |
dc.subject.nsi | VDP::Mathematics and natural scienses: 400::Information and communication science: 420::Simulation, visualisation, signal processing, image analysis: 429 | |
dc.relation.projectID | Norges forskningsråd: 270053 | |
dc.relation.projectID | Norges forskningsråd: 263248 | |