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| rdf:type
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| Description
| - Deformable registration generally relies on the assumption that the sought spatial transformation is smooth. Breathing motion involves sliding motion of the lung with respect to the chest wall. In the case of sliding motion, a discontinuity is present in the motion field and the smoothness assumption can lead to poor matching accuracy. Many authors have proposed alternative registration methods to preserve sliding motion, several of which rely on prior segmentations. We focus on a particular, subanatomical segmentation, called a motion mask, because it is advanta- geous for subsequent registration. The motion mask separates moving from less-moving regions, conveniently allowing to simultaneously estimate the motion for similarly moving tissue. We propose an original method for automatically extracting a motion mask from a CT image of the thorax. The obtained segmentation is useful for any registration method relying on a prior segmentation to account for sliding motion. The method is based on the level set framework, which allows to include geometric priors in the definition of the motion mask. To improve robustness, the original images are simplified and only clear anatomical features are retained, with respect to which the segmentation is defined. The resulting procedure comes down to a monitored level set segmentation of binary images. The method is applied to six inhale-exhale image pairs, and produced satisfying results for all patients, consistent with respect to patient anatomy. We show that the obtained motion masks can facilitate deformable registration of the thorax. By preserving the sliding motion, the complexity of the spatial transformation can be reduced considerably while maintaining matching accuracy.
- Deformable registration generally relies on the assumption that the sought spatial transformation is smooth. Breathing motion involves sliding motion of the lung with respect to the chest wall. In the case of sliding motion, a discontinuity is present in the motion field and the smoothness assumption can lead to poor matching accuracy. Many authors have proposed alternative registration methods to preserve sliding motion, several of which rely on prior segmentations. We focus on a particular, subanatomical segmentation, called a motion mask, because it is advanta- geous for subsequent registration. The motion mask separates moving from less-moving regions, conveniently allowing to simultaneously estimate the motion for similarly moving tissue. We propose an original method for automatically extracting a motion mask from a CT image of the thorax. The obtained segmentation is useful for any registration method relying on a prior segmentation to account for sliding motion. The method is based on the level set framework, which allows to include geometric priors in the definition of the motion mask. To improve robustness, the original images are simplified and only clear anatomical features are retained, with respect to which the segmentation is defined. The resulting procedure comes down to a monitored level set segmentation of binary images. The method is applied to six inhale-exhale image pairs, and produced satisfying results for all patients, consistent with respect to patient anatomy. We show that the obtained motion masks can facilitate deformable registration of the thorax. By preserving the sliding motion, the complexity of the spatial transformation can be reduced considerably while maintaining matching accuracy. (en)
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| Title
| - Automated segmentation of a motion mask to preserve sliding motion in deformable registration of thoracic CT
- Automated segmentation of a motion mask to preserve sliding motion in deformable registration of thoracic CT (en)
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| skos:prefLabel
| - Automated segmentation of a motion mask to preserve sliding motion in deformable registration of thoracic CT
- Automated segmentation of a motion mask to preserve sliding motion in deformable registration of thoracic CT (en)
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| skos:notation
| - RIV/68407700:21230/12:00193281!RIV13-GA0-21230___
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| http://linked.open...avai/predkladatel
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...iv/cisloPeriodika
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/12:00193281
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - motion estimation; registration; spatio-temporal; CT (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
| - US - Spojené státy americké
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/nazevZdroje
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
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| http://linked.open...v/svazekPeriodika
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| http://linked.open...iv/tvurceVysledku
| - Kybic, Jan
- Clarysse, P.
- Rit, S.
- Sarrut, D.
- Vandemeulebroucke, J.
- Bernard, O.
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| http://linked.open...ain/vavai/riv/wos
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| issn
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| number of pages
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| http://bibframe.org/vocab/doi
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| http://localhost/t...ganizacniJednotka
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| is http://linked.open...avai/riv/vysledek
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