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Cardiac segmentation by a velocity-aided active contour model
1Samsung Electronics Co. Ltd., Suwon-city, Gyeonggi-do, South Korea. jinsoo.cho@samsung.com
Insights
Accurate heart disease diagnosis relies on precise cardiac segmentation. A new velocity-aided method using orientation gradient force (OGF) and seed contour tracking (SCT) improves endocardial boundary segmentation in phase contrast MRI, despite some artifact limitations.
Area of Science:
- Medical Imaging
- Cardiovascular Imaging
- Biomedical Engineering
Background:
- Accurate cardiac functional analysis is crucial for diagnosing heart disease, relying heavily on precise myocardial boundary segmentation.
- Current segmentation methods struggle with low image quality, complex heart anatomy, and motion, particularly for the endocardial boundary.
- Existing techniques often lack full automation and struggle with clear delineation between myocardium and adjacent structures.
Purpose of the Study:
- To develop and evaluate a novel velocity-aided cardiac segmentation method to enhance the accuracy of myocardial boundary segmentation, especially the endocardial boundary.
- To improve automatic sequential frame segmentation for cardiac MRI analysis.
- To address limitations in current cardiac segmentation techniques using phase contrast MRI data.
Main Methods:
- A modified active contour model incorporating tensor-based orientation gradient force (OGF) was developed for individual frame segmentation.
- An initial seed contour tracking (SCT) algorithm was integrated for automatic sequential frame segmentation.
- The proposed method was validated using phase contrast MRI data from three healthy human volunteers.
Main Results:
- The OGF method improved accuracy and reproducibility of endocardial boundary segmentation, particularly at the lower left ventricle (LV) level and during end systole.
- The SCT algorithm significantly reduced error propagation in sequential frame segmentation, yielding higher accuracy and reproducibility compared to individual frame segmentation.
- Segmentation improvements were less pronounced at the upper LV level and during end diastole; velocity wrap-around artifacts and blood turbulence degraded performance.
Conclusions:
- The developed velocity-aided cardiac segmentation method shows potential for improving myocardial boundary segmentation accuracy using phase contrast MRI.
- The combination of OGF and SCT enhances automated cardiac segmentation, reducing errors in dynamic analysis.
- Future research should focus on mitigating velocity wrap-around artifacts to further optimize cardiac segmentation performance.
Abstract:
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.
