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Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
2016 (English)In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), IEEE Computer Society, 2016, p. 3146-3149Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi- supervised learning of a graph describing a manifold of anatom- ical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p < 0.001) was obtained compared to direct atlas-based registration. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2016. p. 3146-3149
Keywords [en]
MRI, atlas-based segmentation
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-136004DOI: 10.1109/ICPR.2016.7900118ISI: 000406771303022ISBN: 978-1-5090-4847-2 (electronic)ISBN: 978-1-5090-4848-9 (print)OAI: oai:DiVA.org:liu-136004DiVA, id: diva2:1084329
Conference
International Conference on Pattern Recognition (ICPR)
Available from: 2017-03-24 Created: 2017-03-24 Last updated: 2017-10-05Bibliographically approved

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Andersson, Thord

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Borga, MagnusAndersson, ThordDahlqvist Leinhard, Olof
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Center for Medical Image Science and Visualization (CMIV)Medical InformaticsThe Institute of TechnologyDepartment of Biomedical EngineeringFaculty of Science & EngineeringDivision of Radiological SciencesFaculty of Medicine and Health SciencesDepartment of Radiation Physics
Medical Image Processing

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