Access the full text.
Sign up today, get DeepDyve free for 14 days.
W. Nowinski, A. Thirunavuukarasuu, I. Volkau, Y. Marchenko, B. Aminah, F. Puspitasari, V. Runge (2009)
A Three-Dimensional Interactive Atlas of Cerebral Arterial VariantsNeuroinformatics, 7
(1996)
Animated Dissection of Anatomy for Medicine User’s Guide
W. Wong, T.M. Le, I. Volkau, A. Thirunavuukarasuu, H. Ng, W. Nowinski (2008)
Estimation and presentation of blood flow and velocity from angiographic scans in the human cerebral arterial system2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Y. Qiao, Q. Hu, G. Qian, S. Luo, W. Nowinski (2007)
Thresholding based on variance and intensity contrastPattern Recognit., 40
J. E. Catmull, J. Clark (1978)
Recursively generated B-spline surfaces on arbitrary topological meshesComputer-Aided Design, 10
D. L. Wilson, J. A. Noble (1999)
An adaptive segmentation algorithm for extracting arteries and aneurysms from Time-of-flight MRA dataIEEE Transactions on Medical Imaging, 18
H. U. Doris, D. Kochanek, R. Bartels (1984)
Interpolating splines with local tension, continuity, and bias controlProc. ACM SIGGRAPH, 18
A. Fitzgibbon, M. Pilu, Robert Fisher (1999)
Direct Least Square Fitting of EllipsesIEEE Trans. Pattern Anal. Mach. Intell., 21
J. Dirckx (1998)
Federative Committee on Anatomical Terminology (FCAT). Terminologia Anatomica. International Anatomical TerminologyTerminology, 5
W. L. Nowinski, A. Thirunavuukarasuu, I. Volkau, R. Baimuratov, Q. Hu, A. Aziz (2005b)
Three-dimensional brain atlas of anatomy and vasculatureRadiographics, 25
W. Nowinski, A. Thirunavuukarasuu, I. Volkau, Rafail Baimuratov, Q. Hu, A. Aziz, Su Huang (2005)
Informatics in Radiology (infoRAD): three-dimensional atlas of the brain anatomy and vasculature.Radiographics : a review publication of the Radiological Society of North America, Inc, 25 1
R. Kockro, L. Serra, Yeo Tseng-Tsai, Chumpon Chan, Sitoh Yih-Yian, Chua Gim-Guan, Eugene Lee, Lee Hoe, N. Hern, W. Nowinski (2000)
Planning and simulation of neurosurgery in a virtual reality environment.Neurosurgery, 46 1
V. Runge, Y. Marchenko, I. Volkau, A. Thirunavuukarasuu, W. Nowinski (2008)
Comprar Cerefy Atlas of Cerebral Vasculature/CD-ROM | Val M. Runge | 9781604060904 | Thieme
W. Nowinski, A. Thirunavuukarasuu, R. Bryan (2002)
The Cerefy atlas of brain anatomy : an interactive reference tool for students, teachers, and researchers
W. L. Nowinski, A. Thirunavuukarasuu, R. N. Bryan (2002)
The Cerefy Atlas of Brain Anatomy. An Introduction to Reading Radiological Scans for Students, Teachers, and Researchers
A. L. Rhoton (2007)
Cranial anatomy and surgical approaches
Y. Marchenko, I. Volkau, W. Nowinski (2010)
Vascular Editor: From Angiographic Images to 3D Vascular ModelsJournal of Digital Imaging, 23
P. Huber (1982)
Cerebral angiography
V. Runge, W. Nitz, S. Schmeets (2009)
Book: The Physics of Clinical MR Taught through ImagesThe Neuroradiology Journal, 22
S. Luo, Youliang Zhao (2005)
Extraction of Brain Vessels from Magnetic Resonance Angiographic Images: Concise Literature Review, Challenges, and Proposals2005 IEEE Engineering in Medicine and Biology 27th Annual Conference
E. Catmull, J. Clark (1978)
Recursively generated B-spline surfaces on arbitrary topological meshesSeminal graphics: pioneering efforts that shaped the field
I. Volkau, T. Ng, Y. Marchenko, W. Nowinski (2008)
On Geometric Modeling of the Human Intracranial Venous SystemIEEE Transactions on Medical Imaging, 27
David Douglas, T. Peucker (1973)
ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURECartographica: The International Journal for Geographic Information and Geovisualization, 10
M. Schuenke, E. Schulte, U. Schumacher, L. Ross, E. Lamperti (2007)
Head and neuroanatomy (Thieme Atlas of Anatomy Series)
V. M. Runge, W. R. Nitz, S. H. Schmeets, W. H. Faulkner, N. K. Desai (2004)
The physics of clinical MR taught through images
H. Harnsberger, Andre Macdonald (2006)
Diagnostic and surgical imaging anatomy : brain, head & neck, spine
W. Lorensen, H. Cline (1987)
Marching cubes: A high resolution 3D surface construction algorithmProceedings of the 14th annual conference on Computer graphics and interactive techniques
D. Wilson, J. Noble (1999)
An adaptive segmentation algorithm for time-of-flight MRA dataIEEE Transactions on Medical Imaging, 18
K. H. Hoehne (1995)
VOXEL-MAN, part 1: Brain and skull
T. Sivapatham, M. Vogelbaum (2010)
The Cerefy Atlas of Cerebral Vasculature.Neurosurgery, 67 6
F. Bookstein (1979)
Fitting conic sections to scattered dataComputer Graphics and Image Processing, 9
C. Kirbas, Francis Quek (2003)
Vessel extraction in medical images by 3D wave propagation and tracebackThird IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.
(2001)
MATLAB: Curve fitting toolbox user’s guide
S. Luo, Y. Zhong (2005)
Extraction of brain vessels from magnetic resonance angiographic images: Concise literature review, challenges, and proposalsConference Proceeding IEEE Engineering in Medicine and Biology Society, 2
Nicolas Passat, C. Ronse, J. Baruthio, J. Armspach, C. Maillot, Christine Jahn (2005)
Region‐growing segmentation of brain vessels: An atlas‐based automatic approachJournal of Magnetic Resonance Imaging, 21
A. Rhoton (2008)
Comprar Rhotons Cranial Anatomy and Surgical Approaches | Albert L. Rhoton, Jr MD | 9780781793414 | Lippincott Williams & Wilkins
W. Grand, L. Hopkins, J. Mocco (1999)
Vasculature of the Brain and Cranial Base: Variations in Clinical Anatomy
H. J. Kretschmann, W. Weinrich (2004)
Cranial neuroimaging and clinical neuroanatomy
Liana Lorigo, O. Faugeras, O. Faugeras, W. Grimson, R. Keriven, R. Kikinis, A. Nabavi, C. Westin (2001)
CURVES: Curve evolution for vessel segmentationMedical image analysis, 5 3
I. Volkau, Weili Zheng, Rafail Baimouratov, A. Aziz, W. Nowinski (2005)
Geometric modeling of the human normal cerebral arterial systemIEEE Transactions on Medical Imaging, 24
(1999)
Terminologia anatomica
S. Luo, S. Y. Lee, X. Ma, A. Aziz, Q. Hu, W. L. Nowinski (2005)
Automatic extraction of cerebral arteries: Algorithm and validationProc. Computer Assisted Radiology and Surgery, CARS 2005, 19th International Congress and Exhibition; International Congress Series, 1281
G. Salamon, Y. P. Huang (1976)
Radiological anatomy of the brain
W. L. Nowinski, A. Thirunavuukarasuu, A. L. Benabid (2005a)
The Cerefy Clinical Brain Atlas: Enhanced Edition with Surgical Planning and Intraoperative Support
Pingkun Yan, A. Kassim (2006)
Segmentation of volumetric MRA images by using capillary active contourMedical image analysis, 10 3
D. Kochanek, R. Bartels (1984)
Interpolating splines with local tension, continuity, and bias controlProceedings of the 11th annual conference on Computer graphics and interactive techniques
W. E. Lorensen, H. E. Cline (1987)
Marching cubes: A high resolution 3D surface construction algorithmComputer Graphics (Proceedings of SIGGRAPH ‘87), 21
A. Rosenfeld, J. Pfaltz (1968)
Distance functions on digital picturesPattern Recognit., 1
H. Ralston, D. Ralston (1995)
The digital anatomist: Interactive brain atlasClinical Anatomy, 8
The human cerebrovasculature is extremely complicated and its three dimensional (3D) highly parcellated models, though necessary, are unavailable. We constructed a digital cerebrovascular model from a high resolution, 3T 3D time-of-flight magnetic resonance angiography scan. This model contains the arterial and venous systems and is 3D, geometric, highly parcellated, fully segmented, and completely labeled with name, diameter, and variants. Our approach replaces the tedious and time consuming process of checking and correcting automatic segmentation results done at 2D image level with an aggregate and faster process at 3D model level. The creation of the vascular model required vessel pre-segmentation, centerline extraction, vascular segments connection, centerline smoothing, vessel surface construction, vessel grouping, tracking, editing, labeling, setting diameter, and checking correctness and completeness. For comparison, the same scan was segmented automatically with 59.8% sensitivity and only 16.5% of vessels smaller than 1 pixel size were extracted. To check and correct this automatic segmentation requires 8 weeks. Conversely, the speedup of our approach (the number of 2D segmented areas/the number of 3D vascular segments) is 34. This cerebrovascular model can serve as a reference framework in clinical, research, and educational applications. The wealth of information aggregated with its quantification capabilities can augment or replace numerous textbook chapters. Five applications of the vascular model were described. The model is easily extendable in content, parcellation, and labeling, and the proposed approach is applicable for building a whole body vascular system.
Neuroinformatics – Springer Journals
Published: Nov 18, 2008
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.