Evaluation of optical flow algorithms for tracking endocardial surfaces on three-dimensional ultrasound data

Abstract

With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. A finite element surface was fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency between manual tracing and optical flow estimation on 73% of the entire surface with fewer than 10% difference. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in threedimensional ultrasound data sets.

Fast block flow tracking of atrial septal defects in 4D echocardiography

“…The synthetic image is a parallelepiped of size shx x shy x shz. In the center of image we create an artificial hole using the following formulation for the value vh of each pixel (11) where xh, yh and zh are the x, y and z motions of the artificial ASD. Their directional motions are controlled by parameters a, b and c, with t being the frame number or iteration and φ = (0,t).…”

“…Block matching is fast and simple (Behar et al, 2004), but estimates velocities at low level and lacks robustness. Optical flow has higher sensitivity and specificity, but is very slow and must find a good compromise between local and global displacements (Boukerroui et al, 2003;Duan et al, 2005). The majority of tracking applications are 2D.…”

A New Approach for Automatic Identification of the Ventricular Boundary in Cine Magnetic Resonance Images

“…These methods "track" a known boundary in the first image frame to subsequent frames based on calculated differences between the images. The tracking method based on optical flow 26 might be the most popular one. Optical flow is used to derive a displacement field between two images by matching pixel intensities between the images.…”

“…We divide the state of art literatures into three categories: (1) spatial domain methods; [1][2][3][4][5][6] (2) statistical methods; [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] and (3) time domain tracking methods. [26][27][28][29] Many spatial domain intensity methods utilize a global threshold to accurately identify the ventricular cavity from images which have well-defined differences in pixel intensity between the blood pool and the myocardium. However, the left ventricle often has papillary muscles and rough trabeculations which are included in the ventricular cavity for clinical measurements of volumes and ejection fractions.…”

Atrial Septal Defect Tracking in 3D Cardiac Ultrasound

“…An optical flow approach was proposed in [5] to track endocardial surfaces. The data points are initialized manually and a finite element surface is fitted to the points.…”