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Semi-Supervised Lea...
Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images
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- Borga, Magnus (author)
- Linköpings universitet,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Medicinsk informatik,Tekniska högskolan
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- Andersson, Thord, 1972- (author)
- Linköpings universitet,Institutionen för medicinsk teknik,Tekniska fakulteten,Swedish Defence Research Agency, Sweden
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- Dahlqvist Leinhard, Olof, 1978- (author)
- Linköpings universitet,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Avdelningen för radiologiska vetenskaper,Medicinska fakulteten,Region Östergötland, Radiofysikavdelningen US
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(creator_code:org_t)
- IEEE Computer Society, 2016
- 2016
- English.
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In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). - : IEEE Computer Society. - 9781509048472 - 9781509048489 ; , s. 3146-3149
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Keyword
- MRI
- atlas-based segmentation
Publication and Content Type
- ref (subject category)
- kon (subject category)
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