Segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image registration.
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.
Department of Bioinformatics and Functional Genomics, University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Germany. andreas.biesdorf@bioquant.uni-heidelberg.de
Andreas
A
Biesdorf
Karl
K
Rohr
Duan
D
Feng
Hendrik
H
von Tengg-Kobligk
Fabian
F
Rengier
Dittmar
D
Böckler
Hans-Ulrich
HU
Kauczor
Stefan
S
Wörz
D000465:Algorithms; D001013:Aorta, Thoracic / Q000000981:diagnostic imaging*; D001018:Aortic Diseases / Q000000981:diagnostic imaging*; D001185:Artificial Intelligence; D017023:Coronary Angiography / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*