Extraction of open-state mitral valve geometry from CT volumes
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The importance of mitral valve therapies is rising due to an aging population. Visualization and quantification of the valve anatomy from image acquisitions is an essential component of surgical and interventional planning. The segmentation of the mitral valve from computed tomography (CT) acquisitions is challenging due to high variation in appearance and visibility across subjects. We present a novel semi-automatic approach to segment the open-state valve in 3D CT volumes that combines user-defined landmarks to an initial valve model which is automatically adapted to the image information, even if the image data provide only partial visibility of the valve.
Context information and automatic view initialization are derived from segmentation of the left heart lumina, which incorporates topological, shape and regional information. The valve model is initialized with user-defined landmarks in views generated from the context segmentation and then adapted to the image data in an active surface approach guided by landmarks derived from sheetness analysis. The resulting model is refined by user landmarks.
For evaluation, three clinicians segmented the open valve in 10 CT volumes of patients with mitral valve insufficiency. Despite notable differences in landmark definition, the resulting valve meshes were overall similar in appearance, with a mean surface distance of \(1.62 \pm 2.10\) mm. Each volume could be segmented in 5–22 min.
Our approach enables an expert user to easily segment the open mitral valve in CT data, even when image noise or low contrast limits the visibility of the valve.
KeywordsMitral valve Modeling Segmentation Geometry Computed tomography
This work is part of the BMBF VIP+ project DSS Mitral (partially funded by the German Federal Ministry of Education and Research under Grant 03VP00852).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 4.Klawki R, Schmidt K, Heinemann M (eds) (2016) Deutscher Herzbericht. Deutsche Herzstiftung (German) Google Scholar
- 5.Blausen.com staff (2014) Medical gallery of Blausen Medical 2014. WikiJ Med 1(2):5Google Scholar
- 6.De Bonis M, Al-Attar N, Antunes M, Borger M, Casselman F, Falk V, Folliguet T, Iung B, Lancellotti P, Lentini S, Maisano F, Messika-Zeitoun D, Muneretto C, Pibarot P, Pierard L, Punjabi P, Rosenhek R, Suwalski P, Vahanian A, Wendler O, Prendergast B (2016) Surgical and interventional management of mitral valve regurgitation: a position statement from the European Society of Cardiology Working Groups on cardiovascular surgery and valvular heart disease. Eur Heart J 37:133–139CrossRefPubMedGoogle Scholar
- 7.Kirişli HA, Schaap M, Klein S, Papadopoulou S-L, Bonardi M, Chen C-H, Weustink AC, Mollet NR, Vonken E-J, van der Geest RJ, van Walsum T, Niessen WJ (2010) Evaluation of a multi-atlas based method for segmentation of cardiac CTA data: a large-scale, multicenter, and multivendor study. Med Phys 37(12):6279–6291CrossRefPubMedGoogle Scholar
- 9.Mortazi A, Burt J, Bagci U (2017) Multi-planar deep segmentation networks for cardiac substructures from MRI and CT. arXiv e-printsGoogle Scholar
- 17.Mansi T, Voigt I, Georgescu B, Zheng X, Mengue EA, Hackl M, Ionasec RI, Noack T, Seeburger J, Comaniciu D (2012) An integrated framework for finite-element modeling of mitral valve biomechanics from medical images: application to MitralClip intervention planning. Med Image Anal 16:1330–1346CrossRefPubMedGoogle Scholar
- 18.Grbic S, Easley TF, Mansi T, Bloodworth CH, Pierce EL, Voigt I, Neumann D, Krebs J, Yuh DD, Jensen MO (2014) Multi-modal validation framework of mitral valve geometry and functional computational models. STACOM 2014:239–248Google Scholar
- 29.Pouch AM, Wang H, Takabe M, Jackson B, Gorman J, Gorman R, Yushkevich P, Sehgal C (2014) Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling. Med Image Anal 18:118–129CrossRefPubMedGoogle Scholar
- 31.Graser B, Wald D, Al-Maisary S, Grossgasteiger M, de Simone R, Meinzer H-P, Wolf I (2013) Using a shape prior for robust modeling of the mitral annulus on 4D ultrasound data. Int J Comput Assist Radiol Surg 9(4):635–644Google Scholar
- 32.Wenk JF, Zhang Z, Cheng G, Malhotra D, Acevedo-Bolton G, Burger M, Suzuki T, Saloner DA, Wallace AW, Guccione JM (2010) First finite element model of the left ventricle with mitral valve: insights into ischemic mitral regurgitation. Ann Thorac Surg 89(5):1546–1553CrossRefPubMedPubMedCentralGoogle Scholar
- 34.Mosaliganti K, Gelas A, Cowgill P, Megason S (2009) An optimized N-dimensional hough filter for detecting spherical image objects. Insight JGoogle Scholar
- 35.Lassen B, Kuhnigk J-M, Schmidt M, Krass S, Peitgen H-O (2011) Lung and lung lobe segmentation methods at Fraunhofer MEVIS. In: Fourth international workshop on pulmonary image analysis, vol 2011, pp 185–200Google Scholar
- 38.Wolak A, Gransar H, Thomson LE, Friedman JD, Hachamovitch R, Gutstein A, Shaw LJ, Polk D, Wong ND, Saouaf R, Hayes SW, Rozanski A, Slomka PJ, Germano G, Berman DS (2008) Aortic size assessment by noncontrast cardiac computed tomography: normal limits by age, gender, and body surface area. JACC Cardiovasc Imaging 1:200–209CrossRefPubMedGoogle Scholar