Ultrasound (US) has been increasingly used during interventions, such as cardiac catheterization. To accurately identify the catheter inside US images, extra training for physicians and sonographers is needed. As a consequence, automated segmentation of the catheter in US images and optimized presentation viewing to the physician can be beneficial to accelerate the efficiency and safety of interventions and improve their outcome. For cardiac catheterization, a three-dimensional (3-D) US image is potentially attractive because of no radiation modality and richer spatial information. However, due to a limited spatial resolution of 3-D cardiac US and complex anatomical structures inside the heart, image-based catheter segmentation is challenging. We propose a cardiac catheter segmentation method in 3-D US data through image processing techniques. Our method first applies a voxel-based classification through newly designed multiscale and multidefinition features, which provide a robust catheter voxel segmentation in 3-D US. Second, a modified catheter model fitting is applied to segment the curved catheter in 3-D US images. The proposed method is validated with extensive experiments, using different in-vitro, ex-vivo, and in-vivo datasets. The proposed method can segment the catheter within an average tip-point error that is smaller than the catheter diameter (1.9 mm) in the volumetric images. Based on automated catheter segmentation and combined with optimal viewing, physicians do not have to interpret US images and can focus on the procedure itself to improve the quality of cardiac intervention.
The usage of three-dimensional ultrasound (3D US) during image-guided interventions for e.g. cardiac catheterization has increased recently. To accurately and consistently detect and track catheters or guidewires in the US image during the intervention, additional training of the sonographer or physician is needed. As a result, image-based catheter detection can be beneficial to the sonographer to interpret the position and orientation of a catheter in the 3D US volume. However, due to the limited spatial resolution of 3D cardiac US and complex anatomical structures inside the heart, image-based catheter detection is challenging. In this paper, we study 3D image features for image-based catheter detection using supervised learning methods. To better describe the catheter in 3D US, we extend the Frangi vesselness feature into a multi-scale Objectness feature and a Hessian element feature, which extract more discriminative information about catheter voxels in a 3D US volume. In addition, we introduce a multi-scale statistical 3D feature to enrich and enhance the information for voxel-based classification. Extensive experiments on several in-vitro and ex-vivo datasets show that our proposed features improve the precision to at least 69% when compared to the traditional multi-scale Frangi features (from 45% to 76% at a high recall rate 75%). As for clinical application, the high accuracy of voxel-based classification enables more robust catheter detection in complex anatomical structures.