CNN-based fully automatic mitral valve extraction using CT images and existence probability maps

• URL: http://arxiv.org/abs/2305.00627v2
• Date: Fri, 19 May 2023 00:17:00 GMT
• Title: CNN-based fully automatic mitral valve extraction using CT images and existence probability maps
• Authors: Yukiteru Masuda (1), Ryo Ishikawa (1), Toru Tanaka (1), Gakuto Aoyama (2), Keitaro Kawashima (2), James V. Chapman (3), Masahiko Asami (4), Michael Huy Cuong Pham (5), Klaus Fuglsang Kofoed (5), Takuya Sakaguchi (2), Kiyohide Satoh (1) ((1) Canon Inc., Tokyo, Japan, (2) Canon Medical Systems Corporation, Tochigi, Japan, (3) Canon Medical Informatics, Minnetonka, USA, (4) Division of Cardiology, Mitsui Memorial Hospital, Tokyo, Japan, (5) Department of Cardiology and Radiology, Copenhagen University Hospital - Rigshospitalet & Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark)
• Abstract summary: We propose a fully automated method of extracting mitral valve shape from computed tomography (CT) images. This method extracts the mitral valve shape based on DenseNet using both the original CT image and the existence probability maps of the mitral valve area inferred by U-Net as input. The mean error of shape extraction error in the proposed method is 0.88 mm, which is an improvement of 0.32 mm compared with the method without the existence probability maps.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate extraction of mitral valve shape from clinical tomographic images acquired in patients has proven useful for planning surgical and interventional mitral valve treatments. However, manual extraction of the mitral valve shape is laborious, and the existing automatic extraction methods have not been sufficiently accurate. In this paper, we propose a fully automated method of extracting mitral valve shape from computed tomography (CT) images for the all phases of the cardiac cycle. This method extracts the mitral valve shape based on DenseNet using both the original CT image and the existence probability maps of the mitral valve area inferred by U-Net as input. A total of 1585 CT images from 204 patients with various cardiac diseases including mitral regurgitation (MR) were collected and manually annotated for mitral valve region. The proposed method was trained and evaluated by 10-fold cross validation using the collected data and was compared with the method without the existence probability maps. The mean error of shape extraction error in the proposed method is 0.88 mm, which is an improvement of 0.32 mm compared with the method without the existence probability maps.

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