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Han D, Shim H, Jeon B, Jang Y, Hong Y, Jung S, Ha S, Chang HJ. Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography. PLoS One 2016; 11:e0156837. [PMID: 27536939 PMCID: PMC4990346 DOI: 10.1371/journal.pone.0156837] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 05/22/2016] [Indexed: 11/19/2022] Open
Abstract
We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown.
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Affiliation(s)
- Dongjin Han
- Yonsei University, College of Medicine, 134 Sinchon, Seodaemun, Seoul, Korea
| | - Hackjoon Shim
- Yonsei University, College of Medicine, 134 Sinchon, Seodaemun, Seoul, Korea
| | - Byunghwan Jeon
- Yonsei University, College of Medicine, 134 Sinchon, Seodaemun, Seoul, Korea
| | - Yeonggul Jang
- Yonsei University, College of Medicine, 134 Sinchon, Seodaemun, Seoul, Korea
| | - Youngtaek Hong
- Yonsei University, College of Medicine, 134 Sinchon, Seodaemun, Seoul, Korea
| | - Sunghee Jung
- Yonsei University, College of Medicine, 134 Sinchon, Seodaemun, Seoul, Korea
| | - Seongmin Ha
- Yonsei University, College of Medicine, 134 Sinchon, Seodaemun, Seoul, Korea
| | - Hyuk-Jae Chang
- Yonsei University, College of Medicine, 134 Sinchon, Seodaemun, Seoul, Korea
- * E-mail:
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Saha PK, Strand R, Borgefors G. Digital Topology and Geometry in Medical Imaging: A Survey. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1940-1964. [PMID: 25879908 DOI: 10.1109/tmi.2015.2417112] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.
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Chapman BE, Berty HP, Schulthies SL. Automated generation of directed graphs from vascular segmentations. J Biomed Inform 2015; 56:395-405. [PMID: 26165778 DOI: 10.1016/j.jbi.2015.07.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 07/01/2015] [Accepted: 07/02/2015] [Indexed: 11/18/2022]
Abstract
Automated feature extraction from medical images is an important task in imaging informatics. We describe a graph-based technique for automatically identifying vascular substructures within a vascular tree segmentation. We illustrate our technique using vascular segmentations from computed tomography pulmonary angiography images. The segmentations were acquired in a semi-automated fashion using existing segmentation tools. A 3D parallel thinning algorithm was used to generate the vascular skeleton and then graph-based techniques were used to transform the skeleton to a directed graph with bifurcations and endpoints as nodes in the graph. Machine-learning classifiers were used to automatically prune false vascular structures from the directed graph. Semantic labeling of portions of the graph with pulmonary anatomy (pulmonary trunk and left and right pulmonary arteries) was achieved with high accuracy (percent correct⩾0.97). Least-squares cubic splines of the centerline paths between nodes were computed and were used to extract morphological features of the vascular tree. The graphs were used to automatically obtain diameter measurements that had high correlation (r⩾0.77) with manual measurements made from the same arteries.
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Affiliation(s)
- Brian E Chapman
- University of Utah, Department of Radiology, 729 Arapeen Drive, Salt Lake City, UT 84108, United States.
| | - Holly P Berty
- University of Pittsburgh, Department of Biomedical Informatics, 5607 Baum Boulevard BAUM 423, Pittsburgh, PA 15206-3701, United States
| | - Stuart L Schulthies
- University of Utah, Department of Mathematics, 155 S 1400 E, Salt Lake City, UT 84112-0090, United States
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Han D, Doan NT, Shim H, Jeon B, Lee H, Hong Y, Chang HJ. A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:179-188. [PMID: 25106730 DOI: 10.1016/j.cmpb.2014.07.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Revised: 07/15/2014] [Accepted: 07/16/2014] [Indexed: 06/03/2023]
Abstract
We propose a fast seed detection for automatic tracking of coronary arteries in coronary computed tomographic angiography (CCTA). To detect vessel regions, Hessian-based filtering is combined with a new local geometric feature that is based on the similarity of the consecutive cross-sections perpendicular to the vessel direction. It is in turn founded on the prior knowledge that a vessel segment is shaped like a cylinder in axial slices. To improve computational efficiency, an axial slice, which contains part of three main coronary arteries, is selected and regions of interest (ROIs) are extracted in the slice. Only for the voxels belonging to the ROIs, the proposed geometric feature is calculated. With the seed points, which are the centroids of the detected vessel regions, and their vessel directions, vessel tracking method can be used for artery extraction. Here a particle filtering-based tracking algorithm is tested. Using 19 clinical CCTA datasets, it is demonstrated that the proposed method detects seed points and can be used for full automatic coronary artery extraction. ROC (receiver operating characteristic) curve analysis shows the advantages of the proposed method.
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Affiliation(s)
- Dongjin Han
- Integrative Cardiovascular Imaging Research Center, Yonsei Cardiovascular Center, College of Medicine, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Republic of Korea
| | - Nam-Thai Doan
- Division of Cardiology, Department of Medicine, Cedars-Sinai Heart Institute, 8700 Beverly Boulevard, South Taper Building 1258, Los Angeles, CA 90048, USA
| | - Hackjoon Shim
- Integrative Cardiovascular Imaging Research Center, Yonsei Cardiovascular Center, College of Medicine, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Republic of Korea.
| | - Byunghwan Jeon
- Integrative Cardiovascular Imaging Research Center, Yonsei Cardiovascular Center, College of Medicine, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Republic of Korea
| | - Hyunna Lee
- Department of Brain and Cognitive Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea
| | - Youngtaek Hong
- Integrative Cardiovascular Imaging Research Center, Yonsei Cardiovascular Center, College of Medicine, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Republic of Korea
| | - Hyuk-Jae Chang
- Integrative Cardiovascular Imaging Research Center, Yonsei Cardiovascular Center, College of Medicine, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Republic of Korea
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Tauro F, Grimaldi S, Porfiri M. Unraveling flow patterns through nonlinear manifold learning. PLoS One 2014; 9:e91131. [PMID: 24614890 PMCID: PMC3948738 DOI: 10.1371/journal.pone.0091131] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Accepted: 02/09/2014] [Indexed: 11/18/2022] Open
Abstract
From climatology to biofluidics, the characterization of complex flows relies on computationally expensive kinematic and kinetic measurements. In addition, such big data are difficult to handle in real time, thereby hampering advancements in the area of flow control and distributed sensing. Here, we propose a novel framework for unsupervised characterization of flow patterns through nonlinear manifold learning. Specifically, we apply the isometric feature mapping (Isomap) to experimental video data of the wake past a circular cylinder from steady to turbulent flows. Without direct velocity measurements, we show that manifold topology is intrinsically related to flow regime and that Isomap global coordinates can unravel salient flow features.
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Affiliation(s)
- Flavia Tauro
- Department of Mechanical and Aerospace Engineering, New York University Polytechnic School of Engineering, Brooklyn, New York, United States of America
- Dipartimento di Ingegneria Civile, Edile e Ambientale, Sapienza University of Rome, Rome, Italy
| | - Salvatore Grimaldi
- Department of Mechanical and Aerospace Engineering, New York University Polytechnic School of Engineering, Brooklyn, New York, United States of America
- Dipartimento per l’Innovazione nei Sistemi Biologici, Agroalimentari e Forestali, University of Tuscia, Viterbo, Italy
| | - Maurizio Porfiri
- Department of Mechanical and Aerospace Engineering, New York University Polytechnic School of Engineering, Brooklyn, New York, United States of America
- * E-mail:
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Xu Y, Liang G, Hu G, Yang Y, Geng J, Saha PK. Quantification of coronary arterial stenoses in CTA using fuzzy distance transform. Comput Med Imaging Graph 2012; 36:11-24. [DOI: 10.1016/j.compmedimag.2011.03.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2010] [Revised: 03/15/2011] [Accepted: 03/24/2011] [Indexed: 10/18/2022]
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Abstract
We describe a new method for vascular image analysis that incorporates a generic physiological principle to estimate vessel connectivity, which is a key issue in reconstructing complete vascular trees from image data. We follow Murray's hypothesis of the minimum work principle to formulate the problem as an optimization problem. This principle reflects a global property of any vascular network, in contrast to various local geometric properties adopted as constraints previously. We demonstrate the effectiveness of our method using a set of microCT mouse coronary images. It is shown that the performance of our method has a statistically significant improvement over the widely adopted minimum spanning tree methods that rely on local geometric constraints.
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Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography. Int J Cardiovasc Imaging 2011; 28:921-33. [PMID: 21637981 PMCID: PMC3360862 DOI: 10.1007/s10554-011-9894-2] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2011] [Accepted: 05/19/2011] [Indexed: 01/11/2023]
Abstract
Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality for the visualization of the heart and coronary arteries. To fully exploit the potential of the CCTA datasets and apply it in clinical practice, an automated coronary artery extraction approach is needed. The purpose of this paper is to present and validate a fully automatic centerline extraction algorithm for coronary arteries in CCTA images. The algorithm is based on an improved version of Frangi's vesselness filter which removes unwanted step-edge responses at the boundaries of the cardiac chambers. Building upon this new vesselness filter, the coronary artery extraction pipeline extracts the centerlines of main branches as well as side-branches automatically. This algorithm was first evaluated with a standardized evaluation framework named Rotterdam Coronary Artery Algorithm Evaluation Framework used in the MICCAI Coronary Artery Tracking challenge 2008 (CAT08). It includes 128 reference centerlines which were manually delineated. The average overlap and accuracy measures of our method were 93.7% and 0.30 mm, respectively, which ranked at the 1st and 3rd place compared to five other automatic methods presented in the CAT08. Secondly, in 50 clinical datasets, a total of 100 reference centerlines were generated from lumen contours in the transversal planes which were manually corrected by an expert from the cardiology department. In this evaluation, the average overlap and accuracy were 96.1% and 0.33 mm, respectively. The entire processing time for one dataset is less than 2 min on a standard desktop computer. In conclusion, our newly developed automatic approach can extract coronary arteries in CCTA images with excellent performances in extraction ability and accuracy.
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Chi Y, Liu J, Venkatesh SK, Huang S, Zhou J, Tian Q, Nowinski WL. Segmentation of liver vasculature from contrast enhanced CT images using context-based voting. IEEE Trans Biomed Eng 2010; 58. [PMID: 21095856 DOI: 10.1109/tbme.2010.2093523] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A novel vessel context-based voting is proposed for automatic liver vasculature segmentation in CT images. It is able to conduct full vessel segmentation and recognition of multiple vasculatures effectively. The vessel context describes context information of a voxel related to vessel properties, such as intensity, saliency, direction and connectivity. Voxels are grouped to liver vasculatures hierarchically based on vessel context. They are first grouped locally into vessel branches with the advantage of a vessel junction measurement, and then grouped globally into vasculatures, which is implemented using a multiple feature point voting mechanism. The proposed method has been evaluated on 10 clinical CT datasets. Segmentation of third-order vessel trees from CT images (0.76 × 0.76 × 2.0mm) of the portal venous phase takes less than 3 min on a PC with 2.0 GHz dual core processor and the average segmentation accuracy is up to 98%.
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3D segmentation of coronary arteries based on advanced mathematical morphology techniques. Comput Med Imaging Graph 2010; 34:377-87. [DOI: 10.1016/j.compmedimag.2010.01.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2009] [Revised: 11/16/2009] [Accepted: 01/14/2010] [Indexed: 11/21/2022]
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Jiang Y, Zhuang Z, Sinusas AJ, Papademetris X. Vascular Tree Reconstruction by Minimizing A Physiological Functional Cost. CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION. WORKSHOPS 2010:178-185. [PMID: 21755061 DOI: 10.1109/cvprw.2010.5543593] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The reconstruction of complete vascular trees from medical images has many important applications. Although vessel detection has been extensively investigated, little work has been done on how connect the results to reconstruct the full trees. In this paper, we propose a novel theoretical framework for automatic vessel connection, where the automation is achieved by leveraging constraints from the physiological properties of the vascular trees. In particular, a physiological functional cost for the whole vascular tree is derived and an efficient algorithm is developed to minimize it. The method is generic and can be applied to different vessel detection/segmentation results, e.g. the classic rigid detection method as adopted in this paper. We demonstrate the effectiveness of this method on both 2D and 3D data.
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Affiliation(s)
- Yifeng Jiang
- Diagnostic Radiology, Yale University, New Haven, CT
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Metz CT, Schaap M, Weustink AC, Mollet NR, van Walsum T, Niessen WJ. Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach. Med Phys 2010; 36:5568-79. [PMID: 20095269 DOI: 10.1118/1.3254077] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The application and large-scale evaluation of minimum cost path approaches for coronary centerline extraction from computed tomography coronary angiography (CTCA) data and the development and evaluation of a novel method to reduce the user-interaction time. METHODS A semiautomatic method based on a minimum cost path approach is evaluated for two different cost functions. The first cost function is based on a frequently used vesselness measure and intensity information, and the second is a recently proposed cost function based on region statistics. User interaction is minimized to one or two mouse clicks distally in the coronary artery. The starting point for the minimum cost path search is automatically determined using a newly developed method that finds a point in the center of the aorta in one of the axial slices. This step ensures that all computationally expensive parts of the algorithm can be precomputed. RESULTS The performance of the aorta localization procedure was demonstrated by a success rate of 100% in 75 images. The success rate and accuracy of centerline extraction was quantitatively evaluated on 48 coronary arteries in 12 images by comparing extracted centerlines with a manually annotated reference standard. The method was able to extract 88% and 47% of the vessel center-lines correctly using the vesselness/intensity and region statistics cost function, respectively. For only the proximal part of the vessels these values were 97% and 86%, respectively. Accuracy of centerline extraction, defined as the average distance from correctly automatically extracted parts of the centerline to the reference standard, was 0.64 mm for the vesselness/intensity and 0.51 mm for the region statistics cost function. The interobserver variability was 99% for the success rate measure and 0.42 mm for the accuracy measure. Qualitative evaluation using the best performing cost function resulted in successful centerline extraction for 233 out of the 252 coronaries (92%) in 63 additional CTCA images. CONCLUSIONS The presented results, in combination with minimal user interaction and low computation time, show that minimum cost path approaches can effectively be applied as a preprocessing step for subsequent analysis in clinical practice and biomedical research.
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Affiliation(s)
- C T Metz
- Department of Medical Informatics and Department of Radiology, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
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Schaap M, Metz CT, van Walsum T, van der Giessen AG, Weustink AC, Mollet NR, Bauer C, Bogunović H, Castro C, Deng X, Dikici E, O'Donnell T, Frenay M, Friman O, Hernández Hoyos M, Kitslaar PH, Krissian K, Kühnel C, Luengo-Oroz MA, Orkisz M, Smedby O, Styner M, Szymczak A, Tek H, Wang C, Warfield SK, Zambal S, Zhang Y, Krestin GP, Niessen WJ. Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med Image Anal 2009; 13:701-14. [PMID: 19632885 DOI: 10.1016/j.media.2009.06.003] [Citation(s) in RCA: 175] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2008] [Revised: 04/15/2009] [Accepted: 06/11/2009] [Indexed: 11/25/2022]
Abstract
Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: (1) a method is described to create a consensus centerline with multiple observers, (2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, (3) a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available, and (4) 13 coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms.
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Affiliation(s)
- Michiel Schaap
- Biomedical Imaging Group Rotterdam, Dept. of Radiology and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands.
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Extracting Curve Skeletons from Gray Value Images for Virtual Endoscopy. LECTURE NOTES IN COMPUTER SCIENCE 2008. [DOI: 10.1007/978-3-540-79982-5_43] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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