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Shape-aware label f...
Shape-aware label fusion for multi-atlas frameworks
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- Alvén, Jennifer, 1989 (författare)
- Chalmers University of Technology,Chalmers tekniska högskola
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- Kahl, Fredrik (författare)
- Chalmers University of Technology,Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Lund University Research Groups,Chalmers tekniska högskola
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- Landgren, Matilda (författare)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Lund University Research Groups
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- Larsson, Viktor (författare)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Lund University Research Groups
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- Ulén, Johannes (författare)
- Lund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Enqvist, Olof (författare)
- Chalmers University of Technology,Chalmers tekniska högskola
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(creator_code:org_t)
- Elsevier BV, 2019
- 2019
- Engelska.
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Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655. ; 124, s. 109-117
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving topology and fine structures.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk laboratorie- och mätteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Laboratory and Measurements Technologies (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Medical image segmentation
- Multi-atlas label fusion
- Shape models
- Shape model
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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