Abstract
Accurate morphological information on aortic valve cusps is critical in treatment planning. Image segmentation is necessary to acquire this information, but manual segmentation is tedious and time consuming. In this paper, we propose a fully automatic aortic valve cusps segmentation method from CT images by combining two deep neural networks, spatial configuration-Net for detecting anatomical landmarks and U-Net for segmentation of aortic valve components. A total of 258 CT volumes of end systolic and end diastolic phases, which include cases with and without severe calcifications, were collected and manually annotated for each aortic valve component. The collected CT volumes were split 6:2:2 for the training, validation and test steps, and our method was evaluated by five-fold cross validation. The segmentation was successful for all CT volumes with 69.26 s as mean processing time. For the segmentation results of the aortic root, the right-coronary cusp, the left-coronary cusp and the non-coronary cusp, mean Dice Coefficient were 0.95, 0.70, 0.69, and 0.67, respectively. There were strong correlations between measurement values automatically calculated based on the annotations and those based on the segmentation results. The results suggest that our method can be used to automatically obtain measurement values for aortic valve morphology.
Highlights
Heart valve disease is one of the most common causes of heart failure, which affects approximately 13.2% of people over the age of 75 [1], and the number of patients with valvular disease is increasing due to the global population aging
We proposed an automatic segmentation method for aortic valve cusps that cascades networks for landmark detection and networks for segmentation
We developed an automatic segmentation method for aortic valve cusps from CT
Summary
Heart valve disease is one of the most common causes of heart failure, which affects approximately 13.2% of people over the age of 75 [1], and the number of patients with valvular disease is increasing due to the global population aging. In order to make an operative plan containing procedure and device selections, it is important to obtain accurate information about the valve morphology. The acquisition of valve morphology based on CT images, which has high spatial resolution and provides information on the relationship with surrounding structures, is one of the most important clinical workflows in current clinical situations for treatment planning in aortic valve disease [3]. In order to eliminate these manual operations, it is necessary to have a technique to appropriately segment the target anatomical structures from medical images. Many methods have been reported for segmenting anatomical structures from medical images [4,5,6,7,8,9]. Since the aortic valve is a very thin and fluttering structure, it is expected that applying segmentation methods proposed for other structures to the aortic valve cusps will not provide accurate segmentation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
