Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation
| dc.contributor.author | Huérfano, Y. | |
| dc.contributor.author | Vera, M. | |
| dc.contributor.author | Mar, A. | |
| dc.contributor.author | Bravo, A. | |
| dc.date.accessioned | 2019-03-06T21:46:19Z | |
| dc.date.available | 2019-03-06T21:46:19Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | In this research a three step method for right heart segmentation based on a gradient– based difference operator and machine learning techniques is reported. The proposed method is applied to human heart multi–slice computerized tomography (MSCT) volumes. The first step is the preprocessing, where a gradient–based difference operator is applied to exploit the functional relationship between the original input image and its edge enhanced version. In the second step, the least squares support vector machines (LSSVM) are used with a double purpose. First, an appropriate volume-of-interest is automatically established in order to isolate the structure to segment. Second, another LSSVM is trained for locating the voxels required for initializing the seed based clustering procedure. In the third step (segmentation step), the preprocessed volumes are subsequently processed with an unsupervised clustering technique based on simple linkage region growing. Dice score is used as a metric function to compare the segmentations obtained using the proposed method with respect to ground truth volumes traced by a cardiologist. The right atrium, pulmonary valve, right ventricle and venae cavae are segmented from 80 cardiac MSCT volumes. Reported metrics confirm that this method is a promising technique for right heart segmentation. | eng |
| dc.identifier.issn | 09767673 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12442/2741 | |
| dc.language.iso | eng | eng |
| dc.publisher | IOP Publishing | eng |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional | spa |
| dc.source | Journal of Physics | eng |
| dc.source | IOP Conf. Series: Journal of Physics: Conf. Series 1160 (2019) 012003 | eng |
| dc.source.uri | doi:10.1088/1742-6596/1160/1/012003 | eng |
| dc.subject | Computed tomography | eng |
| dc.subject | Segmentation of the heart | eng |
| dc.subject | Heart - Diseases - Diagnosis | eng |
| dc.title | Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation | eng |
| dc.type | Conference | eng |
| dcterms.references | Petitjean C et al. 2015 Right ventricle segmentation from cardiac MRI: A collation study Med. Image Anal. 19(1) 187 | eng |
| dcterms.references | Zhuang X, Rhode K, Razavi R, Hawkes D and Ourselin S 2010 A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI IEEE Trans. Med. Imag. 29(9) 1612 | eng |
| dcterms.references | Kirisli H et al. 2010 Evaluation of a multi–atlas based method for segmentation of cardiac CTA data: A large–scale, multi–center and multi–vendor study Med. Phys. 37(12) 6279 | eng |
| dcterms.references | Ghesu F, Krubasik E, Georgescu B, Singh V, Zheng Y, Hornegger J and Comaniciu D 2016 Marginal space deep learning: Efficient architecture for volumetric image parsing IEEE Transactions on Medical Imaging 35(5) 1217 | eng |
| dcterms.references | Fernandez N, Gonzalez S, Rodriguez C, Ciobanu C and Saint–Pierre G 2012 FE analysis applied for validation of a biostable aortic valve replacement device: stent and leaflet material selection Int. J. of Biomedical Engineering and Technology 9(4) 378 | eng |
| dcterms.references | Chen S, Kohlberger T and Kirchberg K 2011 Advanced level set segmentation of the right atrium in MR Proc. SPIE 7964 31 | eng |
| dcterms.references | Fuchs A, Kühl J, Lnborg J, Engstrøm T, Vejlstrup N, Køber L and Kofoed K 2012 Automated assessment of heart chamber volumes and function in patients with previous myocardial infarction using multidetector computed tomography Journal of Cardiovascular Computed Tomography 6(5) 325 | eng |
| dcterms.references | Bankman I 2000 Handbook of medical imaging: Processing and analysis (San Diego: Academic Press) | eng |
| dcterms.references | Suykens J, Gestel T and Brabanter JD 2002 Least squares support vector machines (UK: World Scientific Publishing Co.) | eng |
| dcterms.references | Scholkopf B and Smola A 2002 Learning with kernels. Support vector machines, regularization, optimization, and beyond (Massachusetts: The MIT Press) | eng |
| dcterms.references | Vera M et al. 2018 Automatic segmentation of subdural hematomas using a computational technique based on smart operators Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE) (Porto: IEEE) | eng |
| dcterms.references | Dice L 1945 Measures of the amount of ecologic association between species Ecology 26(3) 297 | eng |
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