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We consider cellular-, organ-, and whole organism-levels of biological structures in cardiovascular applications. Several automatic segmentation techniques are presented and discussed in this work. The overall pipeline for reconstruction of biological structures consists of the following steps: image pre-processing, feature detection, initial mask generation, mask processing, and segmentation post-processing. Several examples of image segmentation are presented, including patient-specific abdominal tissues segmentation, vascular network identification and myocyte lipid droplet micro-structure reconstruction.<\/jats:p>","DOI":"10.3390\/computation4030035","type":"journal-article","created":{"date-parts":[[2016,9,15]],"date-time":"2016-09-15T10:22:51Z","timestamp":1473934971000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Image Segmentation for Cardiovascular Biomedical Applications at Different Scales"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4709-4513","authenticated-orcid":false,"given":"Alexander","family":"Danilov","sequence":"first","affiliation":[{"name":"Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina 8, 119333 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman","family":"Pryamonosov","sequence":"additional","affiliation":[{"name":"Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina 8, 119333 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra","family":"Yurova","sequence":"additional","affiliation":[{"name":"Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina 8, 119333 Moscow, Russia"},{"name":"Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Leninskie Gory 1, 119991 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3390\/computation3010072","article-title":"Use of CMEIAS Image Analysis Software to Accurately Compute Attributes of Cell Size, Morphology, Spatial Aggregation and Color Segmentation that Signify in situ Ecophysiological Adaptations in Microbial Biofilm Communities","volume":"3","author":"Dazzo","year":"2015","journal-title":"Computation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"222","DOI":"10.3390\/computation3020222","article-title":"Computational Approach to 3D Modeling of the Lymph Node Geometry","volume":"3","author":"Kislitsyn","year":"2015","journal-title":"Computation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1002\/cnm.1446","article-title":"Modelling pipeline for subject-specific arterial blood flow\u2014A review","volume":"27","author":"Sazonov","year":"2011","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s007910050039","article-title":"Computational vascular fluid dynamics: Problems, models and methods","volume":"2","author":"Quarteroni","year":"2000","journal-title":"Comput. Vis. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.compstruc.2004.03.083","article-title":"Fluid\u2013structure interaction in blood flows on geometries based on medical imaging","volume":"83","author":"Gerbeau","year":"2005","journal-title":"Comput. Struct."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/TIP.2005.860624","article-title":"Segmentation of thin structures in volumetric medical images","volume":"15","author":"Kimmel","year":"2006","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/cnm.1290","article-title":"On the segmentation of vascular geometries from medical images","volume":"26","author":"Radaelli","year":"2010","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1002\/cnm.2600","article-title":"Segmentation of biomedical images using active contour model with robust image feature and shape prior","volume":"30","author":"Yeo","year":"2014","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"ref_9","unstructured":"Rohlfing, T., Brandt, R., Menzel, R., Russakoff, D.B., and Maurer, C.R. (2005). Handbook of Biomedical Image Analysis: Volume III: Registration Models, Springer."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/TMI.2008.2011480","article-title":"Multi-Atlas-Based Segmentation with Local Decision Fusion\u2014 Application to Cardiac and Aortic Segmentation in CT Scans","volume":"28","author":"Isgum","year":"2009","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2012.143","article-title":"Multi-Atlas Segmentation with Joint Label Fusion","volume":"35","author":"Wang","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s00791-003-0101-4","article-title":"Mathematical models and numerical methods for the forward problem in cardiac electrophysiology","volume":"5","author":"Lines","year":"2002","journal-title":"Comput. Vis Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1111\/j.1476-5381.2012.02200.x","article-title":"Computational assessment of drug-induced effects on the electrocardiogram: From ion channel to body surface potentials","volume":"168","author":"Zemzemi","year":"2013","journal-title":"Br. J. Pharmacol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0025-5564(73)90027-8","article-title":"On the one-dimensional theory of blood flow in the larger vessels","volume":"18","author":"Hughes","year":"1973","journal-title":"Math. Biosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1007\/s12265-013-9498-4","article-title":"Computed Fractional Flow Reserve (FFTCT) Derived from Coronary CT Angiography","volume":"6","author":"Zarins","year":"2013","journal-title":"J. Cardiovasc. Transl. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.jcin.2012.08.024","article-title":"Virtual Fractional Flow Reserve from Coronary Angiography: Modeling the Significance of Coronary Lesions","volume":"6","author":"Morris","year":"2013","journal-title":"JACC Cardiovasc. Interv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jsb.2014.08.005","article-title":"Nanoscale three-dimensional imaging of the human myocyte","volume":"188","author":"Sulkin","year":"2014","journal-title":"J. Struct. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_19","unstructured":"Frangi, A., Niessen, W., Vincken, K., and Viergever, M. (1998). Medical Image Computing and Computer-Assisted Interventation \u2013 MICCAI\u201998, Springer."},{"key":"ref_20","unstructured":"Sethian, J. (1999). Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge University Press. Number 3 in Cambridge Monographs on Applied and Computational Mathematics."},{"key":"ref_21","unstructured":"Grady, L. (2006). Computer Vision\u2014ECCV 2006, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1006\/cviu.1998.0680","article-title":"Distance-Ordered Homotopic Thinning: A Skeletonization Algorithm for 3D Digital Images","volume":"72","author":"Pudney","year":"1998","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e02754","DOI":"10.1002\/cnm.2754","article-title":"Methods of Graph Network Reconstruction in Personalized Medicine","volume":"32","author":"Danilov","year":"2016","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1515\/rnam-2012-0024","article-title":"Modelling of bioimpedance measurements: Unstructured mesh application to real human anatomy","volume":"27","author":"Danilov","year":"2012","journal-title":"Russ. J. Numer. Anal. Math. Model."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1515\/rnam-2015-0017","article-title":"Patient-specific anatomical models in human physiology","volume":"30","author":"Vassilevski","year":"2015","journal-title":"Russ. J. Numer. Anal. Math. Model."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Danilov, A.A., Pryamonosov, R.A., and Yurova, A.S. (2016, January 5\u201310). Image segmentation techniques for biomedical modeling: Electrophysiology and hemodynamics. Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2016), Crete Island, Greece.","DOI":"10.7712\/100016.1827.10770"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1007\/s10554-011-9894-2","article-title":"Automatic Centerline Extraction of Coronary Arteries in Coronary Computed Tomographic Angiography","volume":"28","author":"Yang","year":"2012","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_28","unstructured":"Tek, H. Automatic Coronary Tree Modeling. Available online: http:\/\/hdl.handle.net\/10380\/1426."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1148\/radiol.2473070436","article-title":"Cerebral Arteries: Fully Automated Segmentation from CT Angiography\u2014A Feasibility Study 1","volume":"247","author":"Manniesing","year":"2008","journal-title":"Radiology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1007\/s10278-010-9326-1","article-title":"A Fast and Fully Automatic Method for Cerebrovascular Segmentation on Time-of-Flight (TOF) MRA Image","volume":"24","author":"Gao","year":"2011","journal-title":"J. Digit. Imaging"},{"key":"ref_31","unstructured":"Ho, H., Bier, P., Sands, G., and Hunter, P. (2007, January 5\u20137). Cerebral artery segmentation with level set methods. Proceedings of Image and Vision Computing New Zealand 2007, Hamilton, New Zealand."},{"key":"ref_32","unstructured":"Cuisenaire, O. Fully automated segmentation of carotid and vertebral arteries from CTA. Available online: http:\/\/hdl.handle.net\/10380\/3100."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/TMI.2011.2171705","article-title":"Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features","volume":"31","author":"Lucchi","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1109\/TBME.2010.2046485","article-title":"Ranking the Influence of Tissue Conductivities on Forward-Calculated ECGs","volume":"57","author":"Keller","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1097\/01.rct.0000228164.08968.e8","article-title":"Automated Quantification of Body Fat Distribution on Volumetric Computed Tomography","volume":"30","author":"Zhao","year":"2006","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1016\/j.acra.2004.06.005","article-title":"Automated lung segmentation for thoracic CT","volume":"11","author":"Armato","year":"2004","journal-title":"Acad. Radiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.compbiomed.2009.11.020","article-title":"Fast segmentation of bone in CT images using 3D adaptive thresholding","volume":"40","author":"Zhang","year":"2010","journal-title":"Comput. Biol. Med."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chung, H., Cobzas, D., Birdsell, L., Lieffers, J., and Baracos, V. (2009, January 7). Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis. Proceedings of the Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, Lake Buena Vista, FL, USA.","DOI":"10.1117\/12.812412"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1515\/rnam-2013-0025","article-title":"Personalized model adaptation for bioimpedance measurements optimization","volume":"28","author":"Danilov","year":"2013","journal-title":"Russ. J. Numer. Anal. Math. Model."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","first-page":"1","article-title":"Automatic Abdominal Organ Segmentation from CT images","volume":"8","author":"Campadelli","year":"2009","journal-title":"Electron. Lett. Comput. Vis. Image Anal."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active Contours without Edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TPAMI.2013.106","article-title":"A Morphological Approach to Curvature-Based Evolution of Curves and Surfaces","volume":"36","author":"Baumela","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","article-title":"User-Guided 3D Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability","volume":"31","author":"Yushkevich","year":"2006","journal-title":"Neuroimage"},{"key":"ref_45","first-page":"449","article-title":"Removal of Bone in CT Angiography by Multiscale Matched Mask Bone Elimination","volume":"34","author":"Venema","year":"2007","journal-title":"Med. Phys."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/361237.361242","article-title":"Use of the Hough Transformation to Detect Lines and Curves in Pictures","volume":"15","author":"Duda","year":"1972","journal-title":"Commun. ACM"},{"key":"ref_47","unstructured":"Johnson, H.J., McCormick, M.M., and Ibanez, L. (2015). The ITK Software Guide Book 2: Design and Functionality, Kitware, Inc."},{"key":"ref_48","unstructured":"OsiriX DICOM Image Sample Sets. Available online: http:\/\/www.osirix-viewer.com\/datasets."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gamilov, T., Pryamonosov, R., and Simakov, S. (2016, January 5\u201310). Modeling of patient-specific cases of atherosclerosis in carotid arteries. Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2016), Crete Island, Greece.","DOI":"10.7712\/100016.1793.8690"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1515\/rnam-2015-0024","article-title":"Virtual Fractional Flow Reserve Assesment in Patient-Specific Coronary Networks by the 1D Model of Haemodynamics","volume":"30","author":"Gamilov","year":"2015","journal-title":"Russ. J. Numer. Anal. Math. Model."},{"key":"ref_51","unstructured":"Lucchi, A., Smith, K., Achanta, R., Lepetit, V., and Fua, P. (2010). Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2010, Springer."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/4\/3\/35\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:31:00Z","timestamp":1760211060000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/4\/3\/35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,15]]},"references-count":51,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["computation4030035"],"URL":"https:\/\/doi.org\/10.3390\/computation4030035","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,9,15]]}}}