Segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image registration

• Verfasst von: Biesdorf, Andreas [VerfasserIn]
• Verfasst von: Rohr, Karl [VerfasserIn]
• Verfasst von: Feng, Duan [VerfasserIn]
• Verfasst von: Tengg-Kobligk, Hendrik von [VerfasserIn]
• Verfasst von: Rengier, Fabian [VerfasserIn]
• Verfasst von: Böckler, Dittmar [VerfasserIn]
• Verfasst von: Kauczor, Hans-Ulrich [VerfasserIn]
• Verfasst von: Wörz, Stefan [VerfasserIn]
• Titel: Segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image registration
• Verf.angabe: Andreas Biesdorf, Karl Rohr, Duan Feng, Hendrik von Tengg-Kobligk, Fabian Rengier, Dittmar Böckler, Hans-Ulrich Kauczor, Stefan Wörz
• E-Jahr: 2012
• Jahr: 21 June 2012
• Umfang: 15 S.
• Fussnoten: Available online 21 June 2012 ; Gesehen am 18.07.2018
• Titel Quelle: Enthalten in: Medical image analysis
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1996
• Jahr Quelle: 2012
• Band/Heft Quelle: 16(2012), 6, Seite 1187-1201
• ISSN Quelle: 1361-8423
• DOI: doi:10.1016/j.media.2012.05.010
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Aortic arch
• Sach-SW: Elastic image registration
• Sach-SW: Model-based segmentation
• Sach-SW: Parametric intensity model
• Sach-SW: Vessel segmentation
• K10plus-PPN: 1577697251

Abstract

Accurate quantification of the morphology of vessels is important for diagnosis and treatment of cardiovascular diseases. We introduce a new joint segmentation and registration approach for the quantification of the aortic arch morphology that combines 3D model-based segmentation with elastic image registration. With this combination, the approach benefits from the robustness of model-based segmentation and the accuracy of elastic registration. The approach can cope with a large spectrum of vessel shapes and particularly with pathological shapes that deviate significantly from the underlying model used for segmentation. The performance of the approach has been evaluated on the basis of 3D synthetic images, 3D phantom data, and clinical 3D CTA images including pathologies. We also performed a quantitative comparison with previous approaches.