Automatic aorta segmentation and valve landmark detection in C-Arm CT: application to aortic valve implantation

Authors:
Yefeng Zheng;Matthias John;Rui Liao;Jan Boese;Uwe Kirschstein;Bogdan Georgescu;S. Kevin Zhou;Jörg Kempfert;Thomas Walther;Gernot Brockmann;Dorin Comaniciu
Affiliations:
Siemens Corporate Research, Princeton;Siemens AG, Healthcare Sector, Forchheim, Germany;Siemens Corporate Research, Princeton;Siemens AG, Healthcare Sector, Forchheim, Germany;Siemens AG, Healthcare Sector, Forchheim, Germany;Siemens Corporate Research, Princeton;Siemens Corporate Research, Princeton;Department of Cardiac Surgery, Heart Center, University of Leipzig, Germany;Department of Cardiac Surgery, Kerckoff Heart Center, Bad Nauheim, Germany;Department of Cardiovascular Surgery, German Heart Center, Munich, Germany;Siemens Corporate Research, Princeton
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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Abstract

C-arm CT is an emerging imaging technique in transcatheter aortic valve implantation (TAVI) surgery. Automatic aorta segmentation and valve landmark detection in a C-arm CT volume has important applications in TAVI by providing valuable 3D measurements for surgery planning. Overlaying 3D segmentation onto 2D real time fluoroscopic images also provides critical visual guidance during the surgery. In this paper, we present a part-based aorta segmentation approach, which can handle aorta structure variation in case that the aortic arch and descending aorta are missing in the volume. The whole aorta model is split into four parts: aortic root, ascending aorta, aortic arch, and descending aorta. Discriminative learning is applied to train a detector for each part separately to exploit the rich domain knowledge embedded in an expertannotated dataset. Eight important aortic valve landmarks (three aortic hinge points, three commissure points, and two coronary ostia) are also detected automatically in our system. Under the guidance of the detected landmarks, the physicians can deploy the prosthetic valve properly. Our approach is robust under variations of contrast agent. Taking about 1.4 seconds to process one volume, it is also computationally efficient.