Variational segmentation framework in prolate spheroidal coordinates for 3D real-time echocardiography

This paper presents a new formulation of a deformable model segmentation in prolate spheroidal coordinates for segmentation of 3D cardiac echocardiography data. The prolate spheroidal coordinate system enables a representation of the segmented surface with descriptors specifically adapted to the "ellipsoidal" shape of the ventricle. A simple data energy term, based on gray-level information, guides the segmentation. The segmentation framework provides a very fast and simple algorithm to evolve an initial ellipsoidal object towards the endocardial surface of the myocardium with near real-time deformations. With near real-time performance, additional constraints on landmark points, can be used interactively to prevent leakage of the surface

Real-Time Segmentation of 4D Ultrasound by Active Geometric Functions

Four-dimensional ultrasound based on matrix phased array transducers can capture the complex 4D cardiac motion in a complete and real-time fashion. However, the large amount of information residing in 4D ultrasound scans and novel applications under interventional settings pose a big challenge in efficiency for workflow and computer-aided diagnostic algorithms such as segmentation. In this context, a novel formulation framework of the minimal surface problem, called active geometric functions (AGF), is proposed to reach truly real-time performance in segmenting 4D ultrasound data. A specific instance of AGF based on finite element modeling and Hermite surface descriptors was implemented and evaluated on 35 4D ultrasound data sets with a total of 425 time frames. Quantitative comparison to manual tracing showed that the proposed method provides LV contours close to manual segmentation and that the discrepancy was comparable to inter-observer tracing variability. The ability of such realtime segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation

Augmenting CT cardiac roadmaps with segmented streaming ultrasound

Static X-ray computed tomography (CT) volumes are often used as anatomic roadmaps during catheter-based cardiac interventions performed under X-ray fluoroscopy guidance. These CT volumes provide a high-resolution depiction of soft-tissue structures, but at only a single point within the cardiac and respiratory cycles. Augmenting these static CT roadmaps with segmented myocardial borders extracted from live ultrasound (US) provides intra-operative access to real-time dynamic information about the cardiac anatomy. In this work, using a customized segmentation method based on a 3D active mesh, endocardial borders of the left ventricle were extracted from US image streams (4D data sets) at a frame rate of approximately 5 frames per second. The coordinate systems for CT and US modalities were registered using rigid body registration based on manually selected landmarks, and the segmented endocardial surfaces were overlaid onto the CT volume. The root-mean squared fiducial registration error was 3.80 mm. The accuracy of the segmentation was quantitatively evaluated in phantom and human volunteer studies via comparison with manual tracings on 9 randomly selected frames using a finite-element model (the US image resolutions of the phantom and volunteer data were 1.3 x 1.1 x 1.3 mm and 0.70 x 0.82 x 0.77 mm, respectively). This comparison yielded 3.70±2.5 mm (approximately 3 pixels) root-mean squared error (RMSE) in a phantom study and 2.58±1.58 mm (approximately 3 pixels) RMSE in a clinical study. The combination of static anatomical roadmap volumes and dynamic intra-operative anatomic information will enable better guidance and feedback for image-guided minimally invasive cardiac interventions