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.
ASM is another widely used approach in cardiac segmentation. It builds a statistical shape model from a set of training shapes by Principal Component Analysis (PCA). The model is then deform to fit an image in ways similar to the variations observed in the training set. @cite_22 uses an automatically constructed ASM for myocardial segmentation in 3D B-mode echo. @cite_41 uses ASM for MCE myocardial segmentation and they apply a specialized gradient vector flow field to increase its edged capture range. @cite_27 extends ASM by using a joint model of shape, appearance and motion to detect time-continuous LV endocardial contours over time-normalized 2D+t B-mode sequences. The advantage of ASM is that it can account for the myocardial shape variations in our MCE data and provide shape prior to help in cases where the myocardial border is unclear. However, the linear intensity model used by ASM is insufficient to characterize the appearance of MCE data which exhibit huge intensity variations. Therefore, a more powerful non-linear intensity model is required. Furthermore, some of the above methods require good manual initialization of the contour to prevent the solution from getting stuck in poor local minima.
@cite_41 @cite_27 @cite_22
2000828905 2164517592 2084545591 2150040483
Abstract We present a machine learning approach called shape regression machine (SRM) for efficient segmentation of an anatomic structure that exhibits a deformable shape in a medical image, e.g., left ventricle endocardial wall in an echocardiogram. The SRM achieves efficient segmentation via statistical learning of the interrelations among shape, appearance, and anatomy , which are exemplified by an annotated database. The SRM is a two-stage approach. In the first stage that estimates a rigid shape to solve an automatic initialization problem, it derives a regression solution to object detection that needs just one scan in principle and a sparse set of scans in practice, avoiding the exhaustive scanning required by the state-of-the-art classification-based detection approach while yielding comparable detection accuracy. In the second stage that estimates the nonrigid shape, it again learns a nonlinear regressor to directly associate nonrigid shape with image appearance. The underpinning of both stages is a novel image-based boosting ridge regression (IBRR) method that enables multivariate, nonlinear modeling and accommodates fast evaluation. We demonstrate the efficiency and effectiveness of the SRM using experiments on segmenting the left ventricle endocardium from a B-mode echocardiogram of apical four chamber view. The proposed algorithm is able to automatically detect and accurately segment the LV endocardial border in about 120 ms . 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. 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. A new generic model-based segmentation algorithm is presented, which can be trained from examples akin to the active shape model (ASM) approach in order to acquire knowledge about the shape to be segmented and about the gray-level appearance of the object in the image. Whereas ASM alternates between shape and intensity information during search, the proposed approach optimizes for shape and intensity characteristics simultaneously. Local gray-level appearance information at the landmark points extracted from feature images is used to automatically detect a number of plausible candidate locations for each landmark. The shape information is described by multiple landmark-specific statistical models that capture local dependencies between adjacent landmarks on the shape. The shape and intensity models are combined in a single cost function that is optimized noniteratively using dynamic programming, without the need for initialization. The algorithm was validated for segmentation of anatomical structures in chest and hand radiographs. In each experiment, the presented method had a significant higher performance when compared to the ASM schemes. As the method is highly effective, optimally suited for pathological cases and easy to implement, it is highly useful for many medical image segmentation tasks.