Aort kapakçığının çok-kesitli bilgisayarlı tomografi görüntülerinden model-bağimsiz otomatik bölütlenmesi

Yıl 2021, Cilt: 27 Sayı: 2, 122 - 128, 04.04.2021

Devrim Ünay İbrahim Harmankaya İlkay Öksüz , Rahmi Çubuk Levent Çelik Kamuran Kadıpaşaoğlu

Öz

Bir veya birden fazla kalp kapakçığının etkilenebildiği kapakçık hastalıklarının etkin tedavisi için bu kapakçıkların onarılması ya da değiştirilmesini gereklidir. Kapakçıkların 2B/3B statik görüntülerinden elde edilecek bilgiyi tamamlayıcı bilgi içeren hastaya-özgü ve dinamik bir model bu girişimsel tedavi rehberlik edebilir. Bu amaçla bu çalışmada yeni bir otomatik model-bağımsız aort kapakçığı bölütleme yöntemi önerilmiş ve yöntemin doğruluğu aort kapakçığının kapalı anına ait geleneksel kontrastlı EKG-güdümlü çok-kesitli BT verisinden elde edilen uzman işaretlemeleri ile ölçülmüştür. Yöntemin başarısı 19 gerçek veride detaylı olarak değerlendirilmiş ve Hessian temelli sonucun üzerine bölge büyütme yaklaşımının performansının umut vadettiği ama bunun yanı sıra problemin zorluğunu göstermiştir.

Anahtar Kelimeler

Aort kapakçığı, Bölütleme, Model-Bağımsız, Bölge büyütme, Hessian, Supravalvüler sinüs tespiti, Çıkan aort, Bilgisayarlı tomografi, Kalp kapakçığı hastalıkları

Model-Free automatic segmentation of the aortic valve in multislice computed tomography images

Öz

Valvular diseases may affect one or more of the cardiac valves, which may need to be replaced or restored for effective treatment. The surgical procedure can be guided by a patient-specific and dynamic model containing information complementary to the 2D/3D static images of the valves. To this end, in this study a novel automated model-free aortic valve segmentation method is presented, and its performance is evaluated against expert annotations over conventional contrast-enhanced ECG-gated multislice CT data of the aortic valve at its closed position. Detailed evaluation of the proposed method in 19 real cases revealed an encouraging performance of 3D region growing over Hessian based approach but also demonstrated the complexity of the problem.

Anahtar Kelimeler

Aortic valve, Segmentation, Model-Free, Region growing, Hessian, Supravalvular sinus detection, Ascending aorta, Computed tomography, Valvular heart diseases

Kaynakça

• [1] Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, De Simone G, Ferguson TB, Ford E, Furie K, Gillespie C, Go A, Greenlund K, Haase N, Hailpern S, Ho PM, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott MM, Meigs J, Mozaffarian D, Mussolino M, Nichol G, Roger VL, Rosamond W, Sacco R, Sorlie P, Stafford R, Thom T, Wasserthiel-Smoller S, Wong ND, Wylie-Rosett J. “Executive summary: heart disease and stroke statistics-2010 update”. Circulation, 121(7), 948-954, 2010.
• [2] Bouvier E, Logeart D, Sablayrolles JL, Feignoux J, Scheublé C, Touche T, Thabut G, Cohen-Solal A. “Diagnosis of aortic valvular stenosis by multislice cardiac computed tomography”. European Heart Journal, 27(24), 3033-3038, 2006.
• [3] Feuchtner GM, Dichtl W, Friedrich GJ, Frick M, Alber HF, Schachner T, Bonatti JO, Mallouhi A, Frede T, Pachinger O, Nedden DZ, Mueller S. “Multislice computed tomography for detection of patients with aortic valve stenosis and quantification of severity”. Journal of the American College of Cardiology, 47(7), 1410-1417, 2006.
• [4] Frangi AF, Niessen WJ, Viergever MA. “Three-Dimensional modeling for functional analysis of cardiac images: A review”. IEEE Transactions on Medical Imaging, 20(1), 2-25, 2001.
• [5] Frangi AF, Rueckert D, Duncan JS, “Three-Dimensional cardiovascular image analysis”. IEEE Transactions on Medical Imaging, 21(9), 1005-1010, 2002.
• [6] Payer C, Štern D, Bischof H, Urschler M. “Multi-label whole heart segmentation using CNNs and anatomical label configurations”. Lecture Notes in Computer Science, 10663, 190-198, 2018.
• [7] Xu Z, Wu Z, Feng J. “CFUN: Combining faster R-CNN and U-net network for efficient whole heart segmentation”. arXiv, 2018. https://arxiv.org/pdf/1812.04914.pdf .
• [8] Zheng Y, John M, Liao R, Nottling A, Boese J, Kempfert J, Walther T, Brockmann G, Comaniciu D. “Automatic aorta segmentation and valve landmark detection in C-Arm CT for transcatheter aortic valve implantation”. IEEE Transactions on Medical Imaging, 31(12), 2307-2321, 2012.
• [9] Queirós S, Papachristidis A, Morais P, Theodoropoulos K, Fonseca J, Monaghan M, Vilaça J, D'hooge J. “Fully automatic 3-D-TEE segmentation for the planning of transcatheter aortic valve implantation”. IEEE Transactions on Biomedical Engineering, 64(8), 1711-1720, 2017.
• [10] Elattar MA, Wiegerinck E, Planken R, Vanbavel E, van Assen H, Baan J, Marquering H. “Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation”. Medical and Biological Engineering and Computing, 52(7), 611-618, 2014.
• [11] Lorenzo-Valdés M, Sanchez-Ortiz GI, Mohiaddin R, Rueckert D. “Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm”. Lecture Notes in Computer Science, 2878, 440-450, 2003.
• [12] Fritz D, Rinck D, Unterhinninghofen R, Dillmann R, Scheuering M. “Automatic segmentation of the left ventricle and computation of diagnostic parameters using regiongrowing and a statistical model”. Proceedings of SPIE Medical Imaging, 5747, 1844-1851, 2005.
• [13] Ecabert O, Peters J, Weese J. “Modeling shape variability for full heart segmentation in cardiac computed-tomography images”. Proceedings of SPIE Medical Imaging, 6144, 1-8, 2006.
• [14] Lynch M, Ghita O, Whelan PF. “Left-ventricle myocardium segmentation using a coupled level-set with a priori knowledge”. Computerized Medical Imaging and Graphics, 30(4), 255-262, 2006.
• [15] Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D. “Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features”. IEEE Transactions on Medical Imaging, 27(11), 1668-1681, 2008.
• [16] Lin X. Model-Based Strategies for Automated Segmentation of Cardiac Magnetic Resonance İmages. PhD Thesis, University of Auckland, New Zealand, 2008.
• [17] Dong B, Guo Y, Wang B, Gu L. “Aortic valve segmentation from ultrasound images based on shape constraint CV model”. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, 3-7 July 2013.
• [18] Pouch AM, Wang H, Takebe M, Jackson B, Sehgal C, Gorman III J, Gorman R, Yushkevich P. “Automated segmentation and geometrical modeling of the tricuspid aortic valve in 3D echocardiographic images”. Lecture Notes in Computer Science, 8149, 485-492, 2013.
• [19] Ionasec RI, Georgescu B, Gassner E, Vogt S, Kutter O, Scheuering M, Navab N, Comaniciu D. “Dynamic model-driven quantitative and visual evaluation of the aortic valve from 4D CT”. Lecture Notes in Computer Science, 5241, 686-694, 2008.
• [20] Ionasec RI, Voigt I, Georgescu B, Wang Y, Houle H, Higuera F, Navab N, Comaniciu D. “Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE”. IEEE Transactions on Medical Imaging, 29(9), 1636-1651, 2010.
• [21] Weese J, Peters J, Meyer C, Wächter I, Kneser R, Lehmann H, Ecabert O, Barschdorf H, Hanna R, Weber FM, Dössel O, Lorenz C. “The generation of patient-specific heart models for diagnosis and interventions”. Lecture Notes in Computer Science, 6364, 25-35, 2010.
• [22] Grbic S, Ionasec R, Vitanovski D, Voigt I, Wang Y, Georgescu B, Navab N, Comaniciu D. “Complete valvular heart apparatus model from 4D cardiac CT”. Medical Image Analysis, 16(5), 1003-1014, 2012.
• [23] Laissy JP, Messika-Zeitoun D, Serfaty JM, Sebban V, Schouman-Claeys E, Iung B, Vahanian A. “Comprehensive evaluation of preoperative patients with aortic valve stenosis: Usefulness of cardiac multidetector computed tomography”. Heart, 93(9), 1121-1125, 2007.
• [24] Liang L, Kong F, Martin C, Pham T, Wang Q, Duncan J, Sun W. “Machine learning-based 3-D geometry reconstruction and modeling of aortic valve deformation using 3-D computed tomography images”. International Journal for Numerical Methods in Biomedical Engineering, 33(5), 1-13, 2017.
• [25] Segars WP, Mendonca S, Sturgeon G, Tsui BMW. “Enhanced 4D heart model based on high resolution dual source gated cardiac CT images”. IEEE Nuclear Science Symposium Conference Record, Honolulu, Hawaii, 28 October-3 November 2007.
• [26] Boulnois JL, Pechoux T. “Non-invasive cardiac output monitoring by aortic blood flow measurement with the Dynemo 3000”. Journal of Clinical Monitoring and Computing, 16(2), 127-140, 2000.
• [27] Frangi AF, Niessen WJ, Vincken KL, Viergever MA. “Multiscale vessel enhancement filtering”. Lecture Notes in Computer Science, 1496, 130-137, 1998.