Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to data suffering from large intensity variations. In this paper, we demonstrate how to overcome the above limitations of classic RF by presenting a fully automatic segmentation pipeline for myocardial segmentation in full-cycle 2-D MCE data. Specifically, a statistical shape model is used to provide shape prior information that guide the RF segmentation in two ways. First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to refine and constrain the final segmentation to plausible myocardial shapes. We further improve the performance by introducing a bounding box detection algorithm as a preprocessing step in the segmentation pipeline. Our approach on 2-D image is further extended to 2-D+t sequences which ensures temporal consistency in the final sequence segmentations. When evaluated on clinical MCE data sets, our proposed method achieves notable improvement in segmentation accuracy and outperforms other state-of-the-art methods, including the classic RF and its variants, active shape model and image registration.
In this section, we give a brief overview of the segmentation methods used for cardiac ultrasound and highlight their limitations when applied to our MCE data. Current literature focus mainly on either LV segmentation @cite_33 @cite_8 @cite_29 @cite_27 @cite_2 @cite_24 @cite_39 or myocardial segmentation @cite_35 @cite_21 @cite_0 @cite_22 @cite_40 in B-mode echocardiography. There are fewer of them addressing segmentation in MCE @cite_9 @cite_31 @cite_5 @cite_41 .
@cite_35 @cite_33 @cite_22 @cite_8 @cite_41 @cite_29 @cite_21 @cite_9 @cite_39 @cite_24 @cite_0 @cite_27 @cite_40 @cite_2 @cite_5 @cite_31
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We segment long-axis, four-chamber contrast enhanced echocardiography imagery using active shape models. The active shape model algorithm uses principal component analysis to model the shape variability found in a database of training shapes. Accurate segmentation from this model is accomplished by applying a specialized gradient vector flow field to guide the contours to the myocardial borders. The success of the proposed algorithm was verified by application to 65 patient myocardial contrast echocardiography (MCE) studies and through comparison with manually drawn contours. This approach improved accuracy over previously reported data, providing an average accuracy of 0.98, sensitivity of 0.84, specificity of 0.99, and RMSE of 3.3 pixels, when compared to ground truth. Error and variability from automatic segmentation were found to be less than those among multiple human observers. The approach also compares favorably against a standard active contour solution. Cardiac volume function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this paper, different ways of coupling the endocardial and epicardial segmentations are contrasted and compared with uncoupled segmentation. For this purpose, the B-spline explicit active surfaces framework was used; 27 3-D echocardiographic images were used to validate the different coupling strategies, which were compared with manual contouring of the endocardial and epicardial borders performed by an expert. It is shown that an independent segmentation of the endocardium followed by an epicardial segmentation coupled to the endocardium is the most advantageous. In this way, a framework for fully automatic 3-D myocardial segmentation is proposed using a novel coupling strategy. In this paper we propose a novel segmentation technique for quantification of sparsely sampled single-beat 3D contrast enhanced echocardiographic data acquired with a Fast Rotating Ultrasound transducer (FRU). The method uses a 3D Active Shape Model of the Left Ventricle (LV) in combination with local appearance models as prior knowledge to steer the segmentation. From a set of semi-manually delineated contours, 3D meshes of the LV endocardium are constructed for different cardiac phases. Mesh surfaces are partitioned into a fixed number of regions, each of which is modeled by a local image appearance. During segmentation, model update points are generated based on similarity matches with these local appearance models in multiple curved 2D cross-sections, which are then propagated over a dense 3D mesh. The Active Shape Model effectively constrains the shape of the 3D mesh to a statistically plausible cardiac shape. Leave-one-out cross validation was carried out on single-beat contrast enhanced FRU data from 18 patients suffering from various cardiac pathologies. Experiments show that the proposed method generates segmentation results that agree with the ground truth contours with average Point to Point (P2P) error of 4.1±2.0 mm and average Point to Surface (P2S) error of 2.4±2.1mm. Convergence tests show that the proposed method is capable of producing acceptable segmentation results (with less than 1.5X error compared to favorable initialization) within the range of 18 22 mm of in-plane displacement and 12 14 degrees of long-axial orientation error. In this paper, we propose an automatic method for constructing an active shape model (ASM) to segment the complete cardiac left ventricle in 3D ultrasound (3DUS) images, which avoids costly manual landmarking. The automatic construction of the ASM has already been addressed in the literature; however, the direct application of these methods to 3DUS is hampered by a high level of noise and artefacts. Therefore, we propose to construct the ASM by fusing the multidetector computed tomography data, to learn the shape, with the artificially generated 3DUS, in order to learn the neighbourhood of the boundaries. Our artificial images were generated by two approaches: a faster one that does not take into account the geometry of the transducer, and a more comprehensive one, implemented in Field II toolbox. The segmentation accuracy of our ASM was evaluated on 20 patients with left-ventricular asynchrony, demonstrating plausibility of the approach.