Automatic Contour Propagation in Cine Cardiac Magnetic Resonance Images

Abstract

We have developed a method for automatic contour propagation in cine cardiac magnetic resonance images. The method consists of a new active contour model that tries to maintain a constant contour environment by matching gray values in profiles perpendicular to the contour. Consequently, the contours should maintain a constant position with respect to neighboring anatomical structures, such that the resulting contours reflect the preferences of the user. This is particularly important in cine cardiac magnetic resonance images because local image features do not describe the desired contours near the papillary muscle. The accuracy of the propagation result is influenced by several parameters. Because the optimal setting of these parameters is application dependent, we describe how to use full factorial experiments to optimize the parameter setting. We have applied our method to cine cardiac magnetic resonance image sequences from the long axis two-chamber view, the long axis four-chamber view, and the short axis view. We performed our optimization procedure for each contour in each view. Next, we performed an extensive clinical validation of our method on 69 short axis data sets and 38 long axis data sets. In the optimal parameter setting, our propagation method proved to be fast, robust, and accurate. The resulting cardiac contours are positioned within the interobserver ranges of manual segmentation. Consequently, the resulting contours can be used to accurately determine physiological parameters such as stroke volume and ejection fraction.

Optimizing GHT-Based Heart Localization in an Automatic Segmentation Chain

“…In general, the algorithm performance can be optimized by systematically testing parameter value combinations on image databases and selecting those values that minimize a metric of the processing result as used during validation. Hautvast et al [1], for instance, performed full factorial experiments and analyzed the results using the technique of analysis of variances (ANOVA) to optimize parameter settings of their cardiac MR contour segmentation.…”

Left Ventricle Segmentation via Graph Cut Distribution Matching

“…Accurate segmentation of the left ventricle (LV) cavity in magnetic resonance (MR) sequences is very important for complete diagnosis of cardiovascular diseases [1], [3]. Manual segmentation of all images is prohibitively time-consuming.…”

“…Albeit an impressive research effort has been devoted to the LV [1]- [15], current methods are still not sufficiently fast and flexible for routine clinical use, mainly because of the difficulties inherent to MR cardiac images [4]. Existing methods are based, among others, on active contours [1]- [3], [5]- [11], active appearance/shape models [12], [14], and registration [15]. Generally, the problem is stated as an energy optimization.…”

“…Generally, the problem is stated as an energy optimization. In this connection, optimization of active contour functionals has been the most prevalent and flexible choice in the literature because it allows introducing a wide range of photometric and geometric 1 constraints on the solution [1]- [3], [5]- [11]. Generally, these constraints reference a sum over the target region or its boundary of pixelwise correspondences between the image and geometric/photometric models learned from a training set.…”

“…Generally, these constraints reference a sum over the target region or its boundary of pixelwise correspondences between the image and geometric/photometric models learned from a training set. Pixelwise photometric information cannot distinguish connected cardiac regions having almost the same photometric profile [1]- [3], for instance the papillary muscles within the cavity and the myocardium (cf. the examples in Fig.…”