Cardiac segmentation by a velocity-aided active contour model

Heart disease is one of the more life-threatening diseases. Accurate diagnosis and treatment are central to the survival of patients. Numerous diagnostic methods that can assess abnormalities of the heart have been developed. Among these methods, cardiac functional analysis has been widely used to derive cardiac functional parameters that describe the functionality of the heart and are frequently used in diagnosis of various heart diseases. Segmentation of the myocardial boundaries is an essential step for deriving these cardiac functional parameters, and the accuracy of parameters depends much on the correctness of the segmented boundaries. Therefore, it is essential that cardiac segmentation be accurate and reliable. However, current segmentation techniques still have difficulty both extracting accurate myocardial boundaries, especially the endocardial boundary and performing a fully automatic process because of low image quality, the complex shape and motion pattern of the heart, and lack of clear delineation between the myocardium and adjacent anatomic structures.A velocity-aided cardiac segmentation method based a modified active contour model, the tensor-based orientation gradient force (OGF) and phase contrast magnetic resonance imaging (MRI) has been developed to improve the accuracy of segmentation of the myocardial boundaries, especially the endocardial boundary. Furthermore, the initial seed contour tracking (SCT) algorithm has been also developed to improve the accuracy of automatic sequential frame segmentation in conjunction with the OGF-based segmentation method.The performance of the proposed method was assessed by experimentations on a phase contrast MRI data set of three normal human volunteer. The results of the individual frame segmentation showed that the accuracy and reproducibility of segmentation of the endocardial boundary by the use of the OGF was generally improved around the lower level of the LV and end systole. The results of the sequential frame segmentation showed that the propagation of errors caused was significantly reduced by the use of the SCT in addition to the OGF and improvements in the accuracy and reproducibility of segmentation of the endocardial boundary were much higher than the individual frame segmentation. However, improvements were generally negligible around the upper level of the LV and end diastole, and the velocity wrap-around problem and blood turbulence around the basal level of the ventricles even degraded the performance of boundary segmentation. Although this work demonstrates the potential of using the velocity information from phase contrast MRI for cardiac segmentation, the velocity wrap-around artifacts in phase contrast MRI data sets can degrade the performance. Therefore, future work must include the development of appropriate methods to cope with these artifacts.