[1] The neurovascular unit — concept review. Acta Physiol., 210, 790-798(2014).

[2] Neuronal and vascular interactions. Annu. Rev. Neurosci., 38, 25-46(2015).

[3] 3D BrainCV: Simultaneous visualization and analysis of cells and capillaries in a whole mouse brain with one-micron voxel resolution. Neuroimage, 87, 199-208(2014).

[4] Blood vessel segmentation algorithms — review of methods, datasets and evaluation metrics. Comput. Methods Prog. Biomed., 158, 71-91(2018).

[5] A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Med. Image Anal., 13, 819-845(2009).

[6] Learning-based algorithms for vessel tracking: A review. Comput. Med. Imag. Graph., 89, 101840(2021).

[7] Vascular active contour for vessel tree segmentation. IEEE Trans. Biomed. Eng., 58, 1023-1032(2011).

[8] Accurate vessel segmentation with constrained B-snake. IEEE Trans. Image Process., 24, 2440-2455(2015).

[9] Sequential Monte Carlo tracking for marginal artery segmentation on CT angiography by multiple cue fusion. Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 518-525(2013).

[10] Multi-scale vessel enhancement filtering. Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 130-137(1998).

[11] Automatic 2D/3D vessel enhancement in multiple modality images using a weighted symmetry filter. IEEE Trans. Med. Imag., 37, 438-450(2018).

[12] Ensemble of deep learning cascades for segmentation of blood vessels in confocal microscopy images. IEEE Conf. Applied Imagery Pattern Recognition Workshop (AIPR), 1-7(2021).

[13] Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learning. BME Front., 2020, 8620932(2020).

[14] Automatic graph-based modeling of brain microvessels captured with two-photon microscopy. IEEE J. Biomed. Health Inform., 23, 2551-2562(2019).

[15] Deep convolutional neural networks for segmenting 3D in vivo multi-photon images of vasculature in Alzheimer disease mouse models. PLoS One, 14, e0213539(2019).

[16] Attention is all you need. Neural Inf. Process. Syst., 30, 5998-6008(2017).

[17] [17] J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. Yuille, Y. Zhou, “TransUnet: Transformers make strong encoders for medical image segmentation,” preprint (2021), arXiv: 2102. 04306.

[18] [18] Y. Zhang, H. Liu, Q. Hu, “Transfuse: Fusing Transformers and CNNs for medical image segmentation,” preprint (2021), arXiv:2102.08005.

[19] Generative consistency for semi-supervised cerebrovascular segmentation from TOF-MRA. IEEE Trans. Med. Imag., 42, 346-353(2023).

[20] Weakly-supervised cerebrovascular segmentation network with shape prior and model indicator. Int. Conf. Multimedia Retrieval (ICMR), 668-676(2022).

[21] Swin Transformer: Hierarchical vision Transformer using shifted windows. IEEE/CVF Int. Conf. Computer Vision (ICCV), 9992-10002(2021).

[22] A transformer-based framework for automatic COVID19 diagnosis in chest CTs. IEEE/CVF Int. Conf. Computer Vision (ICCV), 513-518(2021).

[23] SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection. IEEE Trans. Circuits Syst. Video Technol., 32, 4486-4497(2022).

[24] [24] H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, M. Wang, “Swin-Unet: Unet-like pure Transformer for medical image segmentation,” preprint (2021), arXiv:2105.05537.

[25] High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level. Nat. Commun., 7, 12142(2016).

[26] High-definition imaging using line-illumination modulation microscopy. Nat. Methods, 18, 309-315(2021).

[27] V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Int. Conf. 3D Vision, 565-571(2016).

[28] MTPA_Unet: Multi-scale Transformer-position attention retinal vessel segmentation network joint Transformer and CNN. Sensors, 22, 4592(2022).

[29] Strided U-Net model: Retinal vessels segmentation using dice loss. Digital Image Computing: Techniques and Applications (DICTA), 1-8(2018).

[30] clDice — a novel topology-preserving loss function for tubular structure segmentation. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 16560-16569(2021).

[31] Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell., 15, 850-863(1993).

[32] [32] O. U. Aydin, A. A. Taha, A. Hilbert, A. A. Khalil, I. Galinovic, J. B. Fiebach, D. Frey, V. I. Madai, “On the usage of average Hausdorff distance for segmentation performance assessment: Hidden bias when used for ranking,” preprint (2020), arXiv:2009.00215.

[33] [33] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger, “3D U-Net: Learning dense volumetric segmentation from sparse annotation,” preprint (2016), arXiv:1606.06650.

[34] Fully convolutional networks for semantic segmentation. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 3431-3440(2015).

[35] VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage, 170, 446-455(2017).