Automatic Four-Chamber Segmentation Using Level-Set Method and Split Energy Function
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Objectives: In this paper, we present an automatic method to segment four chambers by extracting a whole heart, separating the left and right sides of the heart, and spliting the atrium and ventricle regions from each heart in cardiac computed tomography angiography (CTA) efficiently. Methods: We smooth the images by applying filters to remove noise. Next, the volume of interest is detected by using k-means clustering. In this step, the whole heart is coarsely extracted, and it is used for seed volumes in the next step. Then, we detect seed volumes using a geometric analysis based on anatomical information and separate the left and right heart regions with the power watershed algorithm. Finally, we refine the left and right sides of the heart using the level-set method, and extract the atrium and ventricle from the left and right heart regions using the split energy function. Results: We tested the proposed heart segmentation method using 20 clinical scan datasets which were acquired from various patients. To validate the proposed heart segmentation method, we evaluated its accuracy in segmenting four chambers based on four error evaluation metrics. The average values of differences between the manual and automatic segmentations were less than 3.3%, approximately. Conclusions: The proposed method extracts the four chambers of the heart accurately, demonstrating that this approach can assist the cardiologist.
Abstract
Objectives: In this paper, we present an automatic method to segment four chambers by extracting a whole heart, separating the left and right sides of the heart, and spliting the atrium and ventricle regions from each heart in cardiac computed tomography angiography (CTA) efficiently. Methods: We smooth the images by applying filters to remove noise. Next, the volume of interest is detected by using k-means clustering. In this step, the whole heart is coarsely extracted, and it is used for seed volumes in the next step. Then, we detect seed volumes using a geometric analysis based on anatomical information and separate the left and right heart regions with the power watershed algorithm. Finally, we refine the left and right sides of the heart using the level-set method, and extract the atrium and ventricle from the left and right heart regions using the split energy function. Results: We tested the proposed heart segmentation method using 20 clinical scan datasets which were acquired from various patients. To validate the proposed heart segmentation method, we evaluated its accuracy in segmenting four chambers based on four error evaluation metrics. The average values of differences between the manual and automatic segmentations were less than 3.3%, approximately. Conclusions: The proposed method extracts the four chambers of the heart accurately, demonstrating that this approach can assist the cardiologist.
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