21.9.7.2 Brain, Cortex, Cerebral Arteries, Cerebral Aneurysm, Cerebrovascular

Chapter Contents (Back)
Brain. Aneurysms. Cerebral Aneurysm. Arteries. Blood Vessels. General:
See also Aneurysms -- Vascular Analysis.

Suetens, P., Jansen, P., Haegemans, A., Oosterlinck, A., Gybels, J.,
3D Reconstruction of the Blood Vessels of the Brain from a Stereoscopic Pair of Subtraction Angiograms,
IVC(1), No. 1, February 1983, pp. 43-51.
Elsevier DOI BibRef 8302

Suetens, P., Haegemans, A., Oosterlinck, A., Gybels, J.,
An Attempt to Reconstruct the Cerebral Bloodvessels from a Lateral and a Frontal Angiogram,
PR(16), No. 5, 1983, pp. 517-524.
Elsevier DOI 0309
BibRef

Barillot, C.[Christian], Gibaud, B.[Bernard], Scarabin, J.M.[Jean-Marie], and Coatrieux, J.L.[Jean-Louis],
3-D Reconstruction of Cerebral Blood Vessels,
IEEE_CGA(5), No. 12, December, 1985, pp. 13-19. BibRef 8512

Wilson, D.L., Royston, D.D., Noble, J.A., Byrne, J.V.,
Determining X-ray projections for coil treatments of intracranial aneurysms,
MedImg(18), No. 10, October 1999, pp. 973-980.
IEEE Top Reference. 0110
BibRef

Jani, A.B., Pelizzari, C.A., Chen, G.T.Y., Roeske, J., Hamilton, R.J., MacDonald, R.L., Bova, F., Hoffmann, K.R., Sweeney, P.A.,
Volume rendering quantification algorithm for reconstruction of CT volume-rendered structures. 1. Cerebral arteriovenous malformations,
MedImg(19), No. 1, January 2000, pp. 12-24.
IEEE Top Reference. 0110
BibRef

Flasque, N., Desvignes, M., Constans, J.M., Revenu, M.,
Acquisition, segmentation and tracking of the cerebral vascular tree on 3D magnetic resonance angiography images,
MIA(5), No. 3, 2001, pp. 173-183. 3D segmentation, stenosys
PDF File. BibRef 0100

Flasque, N., Desvignes, M.,
Accurate Detection of 3d Tubular Tree Structures,
ICIP00(Vol III: 436-439).
IEEE DOI 0008
BibRef

Bullitt, E., Gerig, G., Pizer, S.M.[Stephen M.], Lin, W.L.[Wei-Li], Aylward, S.R.,
Measuring tortuosity of the intracerebral vasculature from MRA images,
MedImg(22), No. 9, September 2003, pp. 1163-1171.
IEEE Abstract. 0309
BibRef

Cebral, J.R., Castro, M.A., Appanaboyina, S., Putman, C.M., Millan, D., Frangi, A.F.,
Efficient Pipeline for Image-Based Patient-Specific Analysis of Cerebral Aneurysm Hemodynamics: Technique and Sensitivity,
MedImg(24), No. 4, April 2005, pp. 457-467.
IEEE Abstract. 0501
BibRef

Volkau, I., Zheng, W., Baimouratov, R., Aziz, A., Nowinski, W.L.,
Geometric Modeling of the Human Normal Cerebral Arterial System,
MedImg(24), No. 4, April 2005, pp. 529-539.
IEEE Abstract. 0501
BibRef

Millan, R.D., Dempere-Marco, L., Pozo, J.M., Cebral, J.R., Frangi, A.F.,
Morphological Characterization of Intracranial Aneurysms Using 3-D Moment Invariants,
MedImg(26), No. 9, September 2007, pp. 1270-1282.
IEEE DOI 0710

See also Efficient 3D Geometric and Zernike Moments Computation from Unstructured Surface Meshes. BibRef

Volkau, I., Ng, T.T., Marchenko, Y., Nowinski, W.L.,
On Geometric Modeling of the Human Intracranial Venous System,
MedImg(27), No. 6, June 2008, pp. 745-751.
IEEE DOI 0711
BibRef

Zhang, C., Villa-Uriol, M.C., de Craene, M., Pozo, J.M., Frangi, A.F.,
Morphodynamic Analysis of Cerebral Aneurysm Pulsation From Time-Resolved Rotational Angiography,
MedImg(28), No. 7, July 2009, pp. 1105-1116.
IEEE DOI 0906
BibRef

Passat, N., Ronse, C., Baruthio, J., Armspach, J.P., Foucher, J.,
Watershed and multimodal data for brain vessel segmentation: Application to the superior sagittal sinus,
IVC(25), No. 4, April 2007, pp. 512-521.
Elsevier DOI 0702
Vessel segmentation; Watershed segmentation; A priori knowledge integration; Magnetic resonance imaging BibRef

Chillet, D., Passat, N., Jacob-da Col, M.A., Baruthio, J.,
Thickness Estimation of Discrete Tree-Like Tubular Objects: Application to Vessel Quantification,
SCIA05(263-271).
Springer DOI 0506
BibRef

Passat, N., Ronse, C., Baruthio, J., Armspach, J.P., Bosc, M., Foucher, J.,
Using Multimodal MR Data for Segmentation and Topology Recovery of the Cerebral Superficial Venous Tree,
ISVC05(60-67).
Springer DOI 0512
BibRef

Passat, N., Ronse, C., Baruthio, J., Armspach, J.P., Maillot, C.,
Cerebral Vascular Atlas Generation for Anatomical Knowledge Modeling and Segmentation Purpose,
CVPR05(II: 331-337).
IEEE DOI 0507
BibRef

Caldairou, B.[Benoît], Passat, N.[Nicolas], Naegel, B.[Benoît],
Attribute-Filtering and Knowledge Extraction for Vessel Segmentation,
ISVC10(I: 13-22).
Springer DOI 1011
BibRef

Piccinelli, M., Veneziani, A., Steinman, D.A., Remuzzi, A., Antiga, L.,
A Framework for Geometric Analysis of Vascular Structures: Application to Cerebral Aneurysms,
MedImg(28), No. 8, August 2009, pp. 1141-1155.
IEEE DOI 0909
BibRef

O'Sullivan, F., Kirrane, J., Muzi, M., O'Sullivan, J.N., Spence, A.M., Mankoff, D.A., Krohn, K.A.,
Kinetic Quantitation of Cerebral PET-FDG Studies Without Concurrent Blood Sampling: Statistical Recovery of the Arterial Input Function,
MedImg(29), No. 3, March 2010, pp. 610-624.
IEEE DOI 1003
BibRef

Wang, Y., Hu, D., Liu, Y., Li, M.,
Cerebral Artery-Vein Separation Using 0.1-Hz Oscillation in Dual-Wavelength Optical Imaging,
MedImg(30), No. 12, December 2011, pp. 2030-2043.
IEEE DOI 1112
BibRef

Morales, H.G., Larrabide, I., Geers, A.J., San Roman, L., Blasco, J., Macho, J.M., Frangi, A.F.,
A Virtual Coiling Technique for Image-Based Aneurysm Models by Dynamic Path Planning,
MedImg(32), No. 1, January 2013, pp. 119-129.
IEEE DOI 1301
BibRef

Jiang, J., Strother, C.M.,
Interactive Decomposition and Mapping of Saccular Cerebral Aneurysms Using Harmonic Functions: Its First Application With 'Patient-Specific' Computational Fluid Dynamics (CFD) Simulations,
MedImg(32), No. 2, February 2013, pp. 153-164.
IEEE DOI 1301
BibRef

Liu, B.[Bin], Zhao, Q.C.[Qiao-Chu], Dong, J.H.[Jia-Hao], Jia, X.Y.[Xian-Yong], Yue, Z.G.[Zong-Ge],
A stretching transform-based automatic nonrigid registration system for cerebrovascular digital subtraction angiography images,
IJIST(23), No. 2, 2013, pp. 171-187.
DOI Link digital subtraction angiography, cerebrovascular image, non-rigid registration, stretching transformation 1307
BibRef

Mitra, J.[Jubin], Chandra, A.[Abhijit], Halder, T.[Tanmay],
Peak Trekking of Hierarchy Mountain for the Detection of Cerebral Aneurysm using Modified Hough Circle Transform,
ELCVIA(12), No. 1, 2013, pp. xx-yy.
DOI Link 1307
BibRef

Kang, C.K.[Chang-Ki], Park, C.A.[Chan-A], Lee, Y.B.[Yeong-Bae], Park, C.W.[Cheol-Wan], Hong, S.M.[Suk-Min], Kim, Y.B.[Young-Bo], Cho, Z.H.[Zang-Hee],
Micro-vascular imaging experiences of time-of-flight MRA at 7T for cerebrovascular diseases,
IJIST(24), No. 2, 2014, pp. 121-128.
DOI Link 1405
7T MRI, MRA, micro-vascular imaging, cerebrovascular diseases BibRef

Skwerer, S.[Sean], Bullitt, E.[Elizabeth], Huckemann, S.[Stephan], Miller, E.[Ezra], Oguz, I.[Ipek], Owen, M.[Megan], Patrangenaru, V.[Vic], Provan, S.[Scott], Marron, J.S.,
Tree-Oriented Analysis of Brain Artery Structure,
JMIV(50), No. 1-2, September 2014, pp. 126-143.
Springer DOI 1408
BibRef

Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V.C., Shi, L., Heng, P.A.,
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks,
MedImg(35), No. 5, May 2016, pp. 1182-1195.
IEEE DOI 1605
Biomarkers BibRef

Monti, S., Cocozza, S., Borrelli, P., Straub, S., Ladd, M.E., Salvatore, M., Tedeschi, E., Palma, G.,
MAVEN: An Algorithm for Multi-Parametric Automated Segmentation of Brain Veins From Gradient Echo Acquisitions,
MedImg(36), No. 5, May 2017, pp. 1054-1065.
IEEE DOI 1609
Algorithm design and analysis, Diseases, Image segmentation, Magnetic resonance imaging, Manuals, Nonhomogeneous media, Veins, Brain veins, MRI, segmentation, vesselness BibRef

Chon, Y.[Younghoon], Jeong, H.S.[Hyeonseok S.], Im, J.J.[Jooyeon J.], Oh, J.K.[Jin Kyoung], Kim, Y.D.[Young-Do], Chung, Y.A.[Yong-An], Kim, D.J.[Dai-Jin],
Alterations of regional cerebral blood flow after NMDA receptor antagonist administration in patients with alcohol-related dementia,
IJIST(27), No. 4, 2017, pp. 376-382.
DOI Link 1712
alcohol-related dementia, NMDA receptor antagonist, regional cerebral blood flow, SPECT BibRef

Li, R.[Rui], Duan, Y.X.[Yu-Xia], Liu, J.J.[Jin-Jin], Cao, G.Q.[Guo-Quan], Yang, Y.J.[Yun-Jun], Zhuge, Q.C.[Qi-Chuan], Chen, W.J.[Wei-Jian],
Control study of low tube voltage computed tomography angiography (CTA) and digital subtraction angiography (DSA) in diagnosing intracranial micro-aneurysm,
IJIST(28), No. 2, 2018, pp. 86-91.
WWW Link. 1806
BibRef

Moriconi, S., Zuluaga, M.A., Jäger, H.R., Nachev, P., Ourselin, S., Cardoso, M.J.,
Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees,
MedImg(38), No. 1, January 2019, pp. 225-239.
IEEE DOI 1901
Topology, Image segmentation, Kernel, Network topology, Feature extraction, Imaging, connectivity BibRef

Elzaafarany, K.[Khaled], Kumar, G.[Gyanendra], Nakhmani, A.[Arie],
Transcranial Doppler-based modeling of hemodynamics using delay differential equations,
SIViP(13), No. 4, June 2019, pp. 667-673.
Springer DOI 1906
brain aneurysm analysis, diagnosis. BibRef

Hong, J.[Jin], Cheng, H.[Hong], Zhang, Y.D.[Yu-Dong], Liu, J.[Jie],
Detecting cerebral microbleeds with transfer learning,
MVA(30), No. 7-8, October 2019, pp. 1123-1133.
WWW Link. 1911
BibRef

Zeng, Y., Liu, X., Xiao, N., Li, Y., Jiang, Y., Feng, J., Guo, S.,
Automatic Diagnosis Based on Spatial Information Fusion Feature for Intracranial Aneurysm,
MedImg(39), No. 5, May 2020, pp. 1448-1458.
IEEE DOI 2005
Aneurysm, Angiography, Sensitivity, Intracranial aneurysm, deep learning BibRef

Su, J., Wolff, L., van Es, A.C.G.M., van Zwam, W., Majoie, C., Dippel, D.W.J., van der Lugt, A., Niessen, W.J., van Walsum, T.,
Automatic Collateral Scoring From 3D CTA Images,
MedImg(39), No. 6, June 2020, pp. 2190-2200.
IEEE DOI 2006
Deep learning, brain vessel segmentation, 3D CTA images BibRef

Nazir, A., Cheema, M.N., Sheng, B., Li, H., Li, P., Yang, P., Jung, Y., Qin, J., Kim, J., Feng, D.D.,
OFF-eNET: An Optimally Fused Fully End-to-End Network for Automatic Dense Volumetric 3D Intracranial Blood Vessels Segmentation,
IP(29), 2020, pp. 7192-7202.
IEEE DOI 2007
Image segmentation, Biomedical imaging, Convolution, Blood vessels, intracranial vessels segmentation BibRef

Abderezaei, J., Martinez, J., Terem, I., Fabris, G., Pionteck, A., Yang, Y., Holdsworth, S.J., Nael, K., Kurt, M.,
Amplified Flow Imaging (aFlow): A Novel MRI-Based Tool to Unravel the Coupled Dynamics Between the Human Brain and Cerebrovasculature,
MedImg(39), No. 12, December 2020, pp. 4113-4123.
IEEE DOI 2012
Magnetic resonance imaging, Aneurysm, Blood, Strain, Dynamics, Biomedical imaging, Intracranial aneurysm, aneurysm rupture, amplified MRI BibRef

Damseh, R., Delafontaine-Martel, P., Pouliot, P., Cheriet, F., Lesage, F.,
Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks,
MedImg(40), No. 1, January 2021, pp. 381-394.
IEEE DOI 2012
Laplace equations, Computational modeling, Geometry, Imaging, Optimization, two-photonmicroscopy BibRef

Tang, J., Kilic, K., Szabo, T.L., Boas, D.A.,
Improved Color Doppler for Cerebral Blood Flow Axial Velocity Imaging,
MedImg(40), No. 2, February 2021, pp. 758-764.
IEEE DOI 2102
Doppler effect, Ultrasonic imaging, Imaging, Blood flow, Image color analysis, Frequency estimation, ultrasonic brain imaging BibRef

Patera, A.[Alessandra], Zippo, A.G.[Antonio G.], Bonnin, A.[Anne], Stampanoni, M.[Marco], Biella, G.E.M.[Gabriele E. M.],
Brain micro-vasculature imaging: An unsupervised deep learning algorithm for segmenting mouse brain volume probed by high-resolution phase-contrast X-ray tomography,
IJIST(31), No. 3, 2021, pp. 1211-1220.
DOI Link 2108
brain vasculature system, deep learning, X-ray tomography BibRef

Zhu, J.X.[Jun-Xi], Teolis, S.[Spencer], Biassou, N.[Nadia], Tabb, A.[Amy], Jabin, P.E.[Pierre-Emmanuel], Lavi, O.[Orit],
Tracking the Adaptation and Compensation Processes of Patients' Brain Arterial Network to an Evolving Glioblastoma,
PAMI(44), No. 1, January 2022, pp. 488-501.
IEEE DOI 2112
Arteries, Image resolution, Cancer, Blood, Magnetic resonance imaging, Image segmentation, network topology and hemodynamics BibRef

Chen, Y.[Ying], Jin, D.[Darui], Guo, B.[Bin], Bai, X.Z.[Xiang-Zhi],
Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes,
MedImg(41), No. 12, December 2022, pp. 3520-3532.
IEEE DOI 2212
Image segmentation, Training, Feature extraction, Visualization, Biomedical imaging, Task analysis, Cerebrovascular segmentation, TOF-MRA BibRef

Ozaltin, O.[Oznur], Coskun, O.[Orhan], Yeniay, O.[Ozgur], Subasi, A.[Abdulhamit],
Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm,
IJIST(33), No. 1, 2023, pp. 69-91.
DOI Link 2301
classification, CNN, feature extraction, machine learning, NCA, OZNET BibRef

Chen, C.[Cheng], Zhou, K.N.[Kang-Neng], Wang, Z.L.[Zhi-Liang], Xiao, R.X.[Ruo-Xiu],
Generative Consistency for Semi-Supervised Cerebrovascular Segmentation From TOF-MRA,
MedImg(42), No. 2, February 2023, pp. 346-353.
IEEE DOI 2302
Image reconstruction, Biological system modeling, Image segmentation, Perturbation methods, Data models, Training, transformer BibRef

Liu, P.P.[Ping-Ping], Ning, G.J.[Gang-Jun], Shi, L.[Lida], Zhou, Q.Z.[Qiu-Zhan], Chen, X.[Xuan],
Fine-grained classification of intracranial haemorrhage subtypes in head CT scans,
IET-CV(17), No. 2, 2023, pp. 170-188.
DOI Link 2304
compact bilinear pooling, multi-weight focal loss, softmax relative entropy loss, subtype classification of intracranial haemorrhage BibRef

Timmins, K.M.[Kimberley M.], van der Schaaf, I.C.[Irene C.], Vos, I.N.[Iris N.], Ruigrok, Y.M.[Ynte M.], Velthuis, B.K.[Birgitta K.], Kuijf, H.J.[Hugo J.],
Geometric Deep Learning Using Vascular Surface Meshes for Modality-Independent Unruptured Intracranial Aneurysm Detection,
MedImg(42), No. 11, November 2023, pp. 3451-3460.
IEEE DOI 2311
BibRef

Malik, P.[Payal], Vidyarthi, A.[Ankit],
Stacked deep model-based classification of the multiclass brain hemorrhages in CT scans,
IJIST(34), No. 1, 2024, pp. e22955.
DOI Link 2401
brain hemorrhage, computer-aided diagnosis, deep convolutional neural networks, multimodal neurological imaging BibRef

Kothala, L.P.[Lakshmi Prasanna], Guntur, S.R.[Sitaramanjaneya Reddy],
An efficient stacked bidirectional GRU-LSTM network for intracranial hemorrhage detection,
IJIST(34), No. 1, 2024, pp. e22958.
DOI Link 2401
Bi-GRU, Bi-LSTM, computed tomography, deep learning, image classification, intracranial hemorrhage BibRef

Guo, L.[Lilin], Liang, Y.[Yu], Guo, R.[Ruichao], Cao, Z.J.[Zhi-Jian], Ye, J.M.[Jian-Ming], Lai, X.B.[Xiao-Bo],
Staged cluster transformers for intracranial aneurysms segmentation from structure fused 3D MRA,
IJIST(34), No. 2, 2024, pp. e23039.
DOI Link Code:
WWW Link. 2402
deep learning, intracranial aneurysms, medical image, segmentation, transformer BibRef

Ma, W.[Wenao], Chen, C.[Cheng], Gong, Y.Q.[Yu-Qi], Chan, N.Y.[Nga Yan], Jiang, M.[Meirui], Mak, C.H.K.[Calvin Hoi-Kwan], Abrigo, J.M.[Jill M.], Dou, Q.[Qi],
Causal Effect Estimation on Imaging and Clinical Data for Treatment Decision Support of Aneurysmal Subarachnoid Hemorrhage,
MedImg(43), No. 8, August 2024, pp. 2778-2789.
IEEE DOI Code:
WWW Link. 2408
BibRef

Nikseresht, G.[Grant], Agam, G.[Gady], Arfanakis, K.[Konstantinos],
End-to-End Task-Guided Refinement of Synthetic Images for Data Efficient Cerebral Microbleed Detection,
ICPR22(2756-2763)
IEEE DOI 2212
Deep learning, Training, Sensitivity, Refining, Semisupervised learning, Data models BibRef

Autrusseau, F.[Florent], Nader, R.[Rafic], Nouri, A.[Anass], L'Allinec, V.[Vincent], Bourcier, R.[Romain],
Toward a 3D Arterial Tree Bifurcation Model for Intra-Cranial Aneurysm Detection and Segmentation,
ICPR22(4500-4506)
IEEE DOI 2212
Solid modeling, Angiography, Aneurysm, Manuals, Bifurcation, synthetic bifurcation model BibRef

Wu, Y.H.[Yun-Heng], Oda, M.[Masahiro], Hayashi, Y.[Yuichiro], Takebe, T.[Takanori], Nagata, S.[Shogo], Wang, C.[Cheng], Mori, K.[Kensaku],
Blood Vessel Segmentation from Low-Contrast and Wide-Field Optical Microscopic Images of Cranial Window by Attention-Gate-Based Network,
CVMI22(1863-1872)
IEEE DOI 2210
Image segmentation, Visualization, Optical microscopy, Uncertainty, Microscopy, Blood vessels BibRef

Vepa, A.[Arvind], Choi, A.[Andrew], Nakhaei, N.[Noor], Lee, W.J.[Won-Jun], Stier, N.[Noah], Vu, A.[Andrew], Jenkins, G.[Greyson], Yang, X.Y.[Xiao-Yan], Shergill, M.[Manjot], Desphy, M.[Moira], Delao, K.[Kevin], Levy, M.[Mia], Garduno, C.[Cristopher], Nelson, L.[Lacy], Liu, W.[Wandi], Hung, F.[Fan], Scalzo, F.[Fabien],
Weakly-Supervised Convolutional Neural Networks for Vessel Segmentation in Cerebral Angiography,
WACV22(3220-3229)
IEEE DOI 2202
Training, Deep learning, Image segmentation, Costs, Angiography, Network architecture, Semi- and Un- supervised Learning BibRef

Taher, F., Soliman, A., Kandil, H., Mahmoud, A., Shalaby, A., Gimel'farb, G., El-Baz, A.,
Precise Cerebrovascular Segmentation,
ICIP20(394-397)
IEEE DOI 2011
Feature extraction, Data models, Solid modeling, Brain modeling, Blood vessels, Measurement, and MGRF BibRef

Haft-Javaherian, M., Villiger, M., Schaffer, C.B., Nishimura, N., Golland, P., Bouma, B.E.,
A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology,
Microscopy20(4262-4271)
IEEE DOI 2008
Image segmentation, Biological system modeling, Biomedical imaging BibRef

Yang, X., Xia, D., Kin, T., Igarashi, T.,
IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning,
CVPR20(2653-2663)
IEEE DOI 2008
Aneurysm, Solid modeling, Biomedical imaging, Image segmentation, Brain modeling, Shape BibRef

Esfahani, S.S., Zhai, X., Chen, M., Amira, A., Bensaali, F., AbiNahed, J., Dakua, S., Younes, G., Richardson, R.A., Coveney, P.V.,
Hemelb Acceleration and Visualization for Cerebral Aneurysms,
ICIP19(1376-1380)
IEEE DOI 1910
Cerebral aneurysm, HemeLB, Visualization, GPU BibRef

Tang, A.[Alice], Zhang, Z.Y.[Zhi-Yuan], Scalzo, F.[Fabien],
Automatic Registration of Serial Cerebral Angiography: A Comparative Review,
ISVC18(3-14).
Springer DOI 1811
BibRef

Kocinski, M., Materka, A., Elgalal, M., Majos, A.,
On accuracy of personalized 3D-printed MRI-based models of brain arteries,
WSSIP17(1-5)
IEEE DOI 1707
Arteries, Biomedical imaging, Image resolution, Image segmentation, Skeleton, Solid modeling, 3D printing, Centerline-radius modeling, Level set segmentation, Personalized blood vessels phantom, Subpixel, accuracy BibRef