[1] |
GERI O, SHIRAN S I, ROTH J, et al. Vascular territorial segmentation and volumetric blood flow measurement using dynamic contrast enhanced magnetic resonance angiography of the brain[J]. J Cereb Blood Flow Metab, 2017, 37(10): 3446-3456.
doi: 10.1177/0271678X17702394
|
[2] |
TAHER F, PRAKASH N. Automatic cerebrovascular segmentation methods-a review[J]. IAES International Journal of Artificial Intelligence, 2021, 10(3): 576.
|
[3] |
GAO X, UCHIYAMA Y, ZHOU X, et al. A fast and fully automatic method for cerebrovascular segmentation on time-of-flight (TOF) MRA image[J]. J Digit Imaging, 2011, 24(4): 609-625.
doi: 10.1007/s10278-010-9326-1
pmid: 20824304
|
[4] |
CHEN M, GENG C, LI Y X, et al. Automatic detection for cerebral aneurysms in TOF-MRA images based on fuzzy label and deep learning[J]. Chinese J Magn Reson, 2022, 39(3): 267-277.
|
|
陈萌, 耿辰, 李郁欣, 等. 基于模糊标签和深度学习的TOF-MRA影像脑动脉瘤自动检测[J]. 波谱学杂志, 2022, 39(3): 267-277.
|
[5] |
REN Y, CHEN G Z, LIU Z, et al. Reproducibility of image-based computational models of intracranial aneurysm: a comparison between 3D rotational angiography, CT angiography and MR angiography[J]. Biomed Eng Online. 2016, 15: 50.
doi: 10.1186/s12938-016-0163-4
pmid: 27150439
|
[6] |
MU N, LYU Z, REZAEITALESHMAHALLEH M, et al. An attention residual U-Net with differential preprocessing and geometric postprocessing: Learning how to segment vasculature including intracranial aneurysms[J]. Med Image Anal, 2023, 84: 102697.
doi: 10.1016/j.media.2022.102697
|
[7] |
LI Y, NI J, ELAZAB A, et al. Multiple self-attention network for intracranial vessel segmentation[C]// International Joint Conference on Neural Networks, Online: IEEE, 2021: 1-8.
|
[8] |
BIZJAK Ž, CHIEN A, BURNIK I, et al. Novel dataset and evaluation of state-of-the-art vessel segmentation methods[J]. SPIE, 2022, 12032, 120322x.
|
[9] |
XIA L k, ZHANG H, WU Y, et al. 3D vessel-like structure segmentation in medical images by an edge-reinforced network[J]. Med Image Anal, 2022, 82: 102581.
doi: 10.1016/j.media.2022.102581
|
[10] |
JONES J D, CASTANHO P, BAZIRA P, et al. Anatomical variations of the circle of Willis and their prevalence, with a focus on the posterior communicating artery: A literature review and meta-analysis[J]. Clin Anat, 2021, 34(7): 978-990.
doi: 10.1002/ca.v34.7
|
[11] |
TAKEMURA A, SUZUKI M, HARAUCHI H, et al. Automatic segmentation method which divides a cerebral artery tree in time-of-flight MR-angiography into artery segments[J]. P Soc Photo Opt Instrum Eng, 2006, 6144: 1098-1106.
|
[12] |
NOWINSKI W L, VOLKAU I, MARCHENKO Y, et al. A 3D model of human cerebrovasculature derived from 3T magnetic resonance angiography[J]. Neuroinformatics, 2009, 7(1): 23-36.
doi: 10.1007/s12021-008-9028-8
pmid: 19016001
|
[13] |
CHEN L, MOSSA-BASHA M, SUN J, et al. Quantification of morphometry and intensity features of intracranial arteries from 3D TOF MRA using the intracranial artery feature extraction (iCafe): A reproducibility study[J]. Magn Reson Imaging, 2019, 57: 293-302.
doi: S0730-725X(18)30538-1
pmid: 30580079
|
[14] |
CHEN L, SUN J, HIPPE D S, et al. Quantitative assessment of the intracranial vasculature in an older adult population using iCafe[J]. Neurobiology of Aging, 2019, 79: 59-65.
doi: S0197-4580(19)30077-6
pmid: 31026623
|
[15] |
LIU L L, CHENG J H, QUAN Q, et al. A survey on U-shaped networks in medical image segmentations[J]. Neurocomputing, 2020, 409: 244-258.
doi: 10.1016/j.neucom.2020.05.070
|
[16] |
QIU Y, NIE S D, WEI L. Segmentation of breast tumors based on fully convolutional network and dynamic contrast enhanced magnetic resonance image[J]. Chinese J Magn Reson, 2022, 39(2): 196-207.
|
|
邱玥, 聂生东, 魏珑. 基于全卷积网络的乳腺肿瘤动态增强磁共振图像分割[J]. 波谱学杂志, 2022, 39(2): 196-207.
|
[17] |
FAN D P, JI G P, SUN G, et al. Camouflaged object detection[C]// Computer Vision and Pattern Recognition, 2020: 2777-2787
|
[18] |
YANG Z, SOLTANIAN-ZADEH S, FARSIU S. BiconNet: An edge-preserved connectivity-based approach for salient object detection[J]. Pattern Recogn, 2022, 121: 108231.
doi: 10.1016/j.patcog.2021.108231
|
[19] |
LIU Z, LIN Y T, CAO Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]// International Conference on Computer Vision, China:IEEE, 2021: 10012-10022.
|
[20] |
YUSHKEVICH P, PIVEN J, HAZLETT H, et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability[J]. Neuroimage. 2006, 31(3): 1116-28.
doi: 10.1016/j.neuroimage.2006.01.015
pmid: 16545965
|
[21] |
INCI S, ERBENGI A, ÖZGEN T. Aneurysms of the distal anterior cerebral artery: report of 14 cases and a review of the literature[J]. Surg Neurol, 1998, 50(2): 130-140.
pmid: 9701118
|
[22] |
CANNY J. A computational approach to edge detection[J]. IEEE Trans Pattern Anal Mach Intell, 1986, 8(6): 679-98.
|
[23] |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Computer Vision and Pattern Recognition, USA: IEEE, 2016: 770-778.
|
[24] |
WOO S H, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[J]. Computer Vision, 2018, 11211: 3-19.
|
[25] |
YEUNG M, SALA E, SCHÖNLIEB C B, et al. Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation[J]. Comput Med Imag Grap, 2022, 95: 102026.
doi: 10.1016/j.compmedimag.2021.102026
|
[26] |
ISENSEE F, JAEGER P F, KOHL S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nat Methods, 2021, 18(2): 203-211.
doi: 10.1038/s41592-020-01008-z
pmid: 33288961
|
[27] |
ISENSEE F, KICKINGEREDER P, WICK W, et al. Brain tumor segmentation and radiomics survival prediction: Contribution to the BRATS 2017 Challenge[J]. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2017, 10670: 287-297.
|
[28] |
MILLETARI F, NAVAB N, AHMADI S A, et al. V-Net: Fully convolutional neural networks for volumetric medical image segmentation[C]// International Conference on 3d Vision, 2016: 565-571.
|
[29] |
ÇIÇEK Ö, ABDULKADIR A, LIENKAMP S S. 3D U-Net: Learning dense volumetric segmentation from sparse annotation[C]// Medical Image Computing and Computer-Assisted Intervention, 2016: 424-432.
|