Automatic Aorta Segmentation and Valve Landmark Detection in C-Arm CT for Transcatheter Aortic Valve Implantation

Abstract

Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure to treat severe aortic valve stenosis. As an emerging imaging technique, C-arm computed tomography (CT) plays a more and more important role in TAVI on both pre-operative surgical planning (e.g., providing 3-D valve measurements) and intra-operative guidance (e.g., determining a proper C-arm angulation). Automatic aorta segmentation and aortic valve landmark detection in a C-arm CT volume facilitate the seamless integration of C-arm CT into the TAVI workflow and improve the patient care. In this paper, we present a part-based aorta segmentation approach, which can handle structural variation of the aorta 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 expert-annotated dataset. Eight important aortic valve landmarks (three hinges, three commissures, and two coronary ostia) are also detected automatically with an efficient hierarchical approach. Our approach is robust under all kinds of variations observed in a real clinical setting, including changes in the field-of-view, contrast agent injection, scan timing, and aortic valve regurgitation. Taking about 1.1 s to process a volume, it is also computationally efficient. Under the guidance of the automatically extracted patient-specific aorta model, the physicians can properly determine the C-arm angulation and deploy the prosthetic valve. Promising outcomes have been achieved in real clinical applications.

Machine learning–based 3‐D geometry reconstruction and modeling of aortic valve deformation using 3‐D computed tomography images

“…The landmark detection accuracy achieved with our proposed method is compared with that obtained by Zheng et al (17) in Table-1. The results demonstrate the ability of the landmark detectors built upon the local shape dictionaries.…”

“…These shape dictionaries eliminate the need for feature location searching during the training of the classifiers, and therefore only a relatively small amount of training data is required, compared to the related methods (17, 19) which used hundreds of training images. Once the type of image features is determined (e.g.…”

“…image gradient), image feature selection is used to find the optimal locations in the images to extract those image features. In the literature (13, 17, 32, 38), feature selection is usually conducted in a brute-force search manner, and a substantial amount of training data is required to find the features that are statistically significant. Using our method, since we have the dictionaries of the local shapes, we only need to extract image features at the points of the local shapes, and image features at other locations are simply irrelevant or unnecessary.…”

“…There are a few works related to valve shape estimation. Zheng et al (17) developed a method for aorta segmentation and landmark detection from 3D C-Arm CT images, which was aimed for 3D visualization during transapical aortic valve implantation. Pouch et.al.…”

Automated Segmentation and Geometrical Modeling of the Tricuspid Aortic Valve in 3D Echocardiographic Images

“…A few automated methods have been developed to segment the aortic root in MSCT data [8-9], but they do not delineate the aortic leaflets (also referred to as cusps). One proposed segmentation method parametrically represents both the 3D aortic root and leaflet geometry in MSCT images [10].…”