[1] J. Zhang, Z. Tan, L. Lin et al. Recent advances in monitoring blood flow with laser speckle imaging. Chin. J. Laser Med. Surg., 25, 233-245(2016).
[2] I. Cordovil, G. Huguenin, G. Rosa, A. Bello, O. Köhler, R. de Moraes, E. Tibiriçá. Evaluation of systemic microvascular endothelial function using laser speckle contrast imaging. Microvasc. Res., 83, 376-379(2012).
[3] R. Chen, P. Miao, S. Tong. Transmissive multifocal laser speckle contrast imaging through thick tissue. J. Innov. Opt. Heal. Sci., 16, 2350005(2023).
[4] P. A. D. Timoshina, A. N. Bashkatov, D. A. Alexandrov, V. I. Kochubey, V. V. Tuchin. Laser speckle contrast imaging for monitoring of acute pancreatitis at ischemiareperfusion injury of the pancreas in rats. J. Innov. Opt. Heal. Sci., 15, 2242002(2022).
[5] X. Huang, D. Tang, P. Chen, L. Chen, X. Li, X. Wang. Laser speckle contrast imaging to predict the effect of temporary spinal cord stimulation in postherpetic neuralgia patients: A prospective observational study. J. Innov. Opt. Heal. Sci., 17, 2350014(2024).
[6] A. Aminfar, N. Davoodzadeh, G. Aguilar. Application of optical flow algorithms to laser speckle imaging. Microvasc. Res., 122, 52-59(2019).
[7] P. G. Vaz, A. Humeau-Heurtier, E. Figueiras, C. Correia, J. Cardoso. Laser speckle imaging to monitor microvascular blood flow: A review. IEEE Rev. Biomed. Eng., 9, 106-120(2016).
[8] S. Hirose, W. Saito, K. Yoshida, M. Saito, Z. Dong, K. Namba, H. Satoh, S. Ohno. Elevated choroidal blood flow velocity during systemic corticosteroid therapy in Vogt–Koyanagi–Harada disease. Acta Ophthalmol., 86, 902-907(2008).
[9] A. I. Srienc, Z. L. Kurth-Nelson, E. A. Newman. Imaging retinal blood flow with laser speckle flowmetry. Front. in Neuroene., 2, 128(2010).
[10] M. Wang, W. Mao, C. Guan, G. Feng, H. Tan, D. Han, Y. Zeng. Full-field functional optical angiography. Opt. Lett., 42, 635-638(2017).
[11] M. M. Qureshi, Y. Liu, K. D. Mac, M. Kim, A. M. Safi, E. Chung. Quantitative blood flow estimation in vivo by optical speckle image velocimetry. Optica, 8, 1092-1101(2021).
[12] W. Ma, S. Yu, K. Ma, J. Wang, X. Ding, Y. Zheng. Multi-task neural networks with spatial activation for retinal vessel segmentation and artery/vein classification. Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd Int. Conf., 769-778(2019).
[13] A. B. Parthasarathy, E. L. Weber, L. M. Richards, D. J. Fox, A. K. Dunn. Laser speckle contrast imaging of cerebral blood flow in humans during neurosurgery: A pilot clinical study. J. Biomed. Opt., 15, 066030(2010).
[14] Z. Fan, J. Mo, B. Qiu, W. Li, G. Zhu, C. Li, J. Hu, Y. Rong, X. Chen. Accurate retinal vessel segmentation via octave convolution neural network(2019).
[15] P. Carmeliet, R. K. Jain. Angiogenesis in cancer and other diseases. Nature, 407, 249-257(2000).
[16] P. A. Campochiaro. Molecular pathogenesis of retinal and choroidal vascular diseases. Prog. Retin. Eye Res., 49, 67-81(2015).
[17] E. De Momi, C. Caborni, F. Cardinale, G. Casaceli, L. Castana, M. Cossu, R. Mai, F. Gozzo, S. Francione, L. Tassi et al. Multi-trajectories automatic planner for stereoelectroencephalography (SEEG). Int. J. Comput. Ass. Rad., 9, 1087-1097(2014).
[18] C. Faria, O. Sadowsky, E. Bicho, G. Ferrigno, L. Joskowicz, M. Shoham, R. Vivanti, E. De Momi. Validation of a stereo camera system to quantify brain deformation due to breathing and pulsatility. Med. Phys., 41, 113502(2014).
[19] P. Rodrigues, P. Guimaraes, T. Santos, S. Simao, T. Miranda, P. Serranho, R. Bernardes. Two-dimensional segmentation of the retinal vascular network from optical coherence tomography. J. Biomed. Opt., 18, 126011(2013).
[20] Y. Ma, H. Hao, J. Xie, H. Fu, J. Zhang, J. Yang, Z. Wang, J. Liu, Y. Zheng, Y. Zhao. ROSE: A retinal OCT-angiography vessel segmentation dataset and new model. IEEE Trans. Med. Imag., 40, 928-939(2020).
[21] M. Li, Y. Chen, Z. Ji, K. Xie, S. Yuan, Q. Chen, S. Li. Image projection network: 3D to 2D image segmentation in OCTA images. IEEE Trans. Med. Imag., 39, 3343-3354(2020).
[22] F. Xu, X.-C. Wang, M.-Q. Zhou, Z. Wu, X.-Y. Liu. Segmentation algorithm of brain vessel image based on SEM statistical mixture model. 2010 Seventh Int. Conf. Fuzzy Systems and Knowledge Discovery, 1830-1833(2010).
[23] S. Hanaoka, Y. Nomura, M. Nemoto, S. Miki, T. Yoshikawa, N. Hayashi, K. Ohtomo, Y. Masutani, A. Shimizu. Hotpig: A novel geometrical feature for vessel morphometry and its application to cerebral aneurysm detection. Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention–MICCAI 2015: 18th International Conference, 103-110(2015).
[24] D. Robben, E. Türetken, S. Sunaert, V. Thijs, G. Wilms, P. Fua, F. Maes, P. Suetens. Simultaneous segmentation and anatomical labeling of the cerebral vasculature. Med. Image Anal., 32, 201-215(2016).
[25] M. W. Law, A. C. Chung. An oriented flux symmetry based active contour model for three dimensional vessel segmentation. Computer Vision–ECCV 2010: 11th European Conf. Computer Vision, 720-734(2010).
[26] Y.-Z. Zeng, Y.-Q. Zhao, P. Tang, M. Liao, Y.-X. Liang, S.-H. Liao, B.-J. Zou. Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts. Comput. Meth. Prog. Bio., 150, 31-39(2017).
[27] S. Moccia, E. De Momi, S. El Hadji, L. S. Mattos. Blood vessel segmentation algorithms—review of methods, datasets and evaluation metrics. Comput. Methods Prog. Biomed., 158, 71-91(2018).
[28] S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, M. Goldbaum. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imag., 8, 263-269(1989).
[29] A. F. Frangi, W. J. Niessen, K. L. Vincken, M. A. Viergever. Multiscale vessel enhancement filtering. Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention–MICCAI’98: First Int. Conf., 130-137(1998).
[30] K. Krissian, G. Malandain, N. Ayache. Directional anisotropic diffusion applied to segmentation of vessels in 3d images. Int. Conf. Scale-Space Theories in Computer Vision, 345-348(1997).
[31] O. Chutatape, L. Zheng, S. M. Krishnan. Retinal blood vessel detection and tracking by matched gaussian and kalman filters. Proc. 20th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society., 20, 3144-3149(1998).
[32] T. McInerney, D. Terzopoulos. Deformable models in medical image analysis: A survey. Med. Image Anal., 1, 91-108(1996).
[33] C. Xu, D. L. Pham, J. L. Prince. Image segmentation using deformable models. Handbook of Medical Imaging(2000).
[34] S. Asgari Taghanaki, K. Abhishek, J. P. Cohen, J. Cohen-Adad, G. Hamarneh. Deep semantic segmentation of natural and medical images: A review. Artif. Intell. Rev., 54, 137-178(2021).
[35] N. Shen, T. Xu, Z. Bian, S. Huang, F. Mu, B. Huang, Y. Xiao, J. Li. Scanet: A unified semi-supervised learning framework for vessel segmentation. IEEE Trans. Med. Imag., 42, 2476-2489(2023).
[36] T. A. Soomro, A. J. Afifi, L. Zheng, S. Soomro, J. Gao, O. Hellwich, M. Paul. Deep learning models for retinal blood vessels segmentation: A review. IEEE Access, 7, 71696-71717(2019).
[37] S. Albawi, T. A. Mohammed, S. Al-Zawi. Understanding of a convolutional neural network. Int. Conf. Engineering and Technology (ICET), 1-6(2017).
[38] S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, G. Yang. Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing, 149, 708-717(2015).
[39] P. Prentašić, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunić, S. Lončarić. Segmentation of the foveal microvasculature using deep learning networks. J. Biomed. Opt., 21, 075008(2016).
[40] E. Smistad, L. Løvstakken. Vessel detection in ultrasound images using deep convolutional neural networks. Int. Workshop on Deep Learning in Med. Image Anal., 30-38(2016).
[41] P. Liskowski, K. Krawiec. Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imag., 35, 2369-2380(2016).
[42] M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, S. A. Barman. Blood vessel segmentation methodologies in retinal images–a survey. Comput. Methods Prog. Biomed., 108, 407-433(2012).
[43] L. Zhao, Y. Li, H. Lu, L. Yuan, S. Tong. Separation of cortical arteries and veins in optical neurovascular imaging. J. Innov. Opt. Heal. Sci., 7, 1350069(2014).
[44] H. Chen, Y. Shi, B. Bo, D. Zhao, P. Miao, S. Tong, C. Wang. Real-time cerebral vessel segmentation in laser speckle contrast image based on unsupervised domain adaptation. Front. Neurosci., 15, 755198(2021).
[45] S. Fu, J. Xu, S. Chang, L. Yang, S. Ling, J. Cai, Q. Zhao. Robust vascular segmentation for raw complex images of laser speckle contrast based on weakly supervised learning. IEEE Trans. Med. Imag., 43(2023).
[46] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, B. Van Ginneken. Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag., 23, 501-509(2004).
[47] S. Guo, Z. Yan, K. Zhang, W. Zuo, L. Zhang. Toward convolutional blind denoising of real photographs. Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 1712-1722(2019).
[48] A. B. Molini, D. Valsesia, G. Fracastoro, E. Magli. Speckle2Void: Deep self-supervised SAR despeckling with blind-spot convolutional neural networks. IEEE Trans. Geosci. Remote Sens., 60, 1-17(2021).
[49] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network(2015).
[50] X. Li, L. Yu, H. Chen, C.-W. Fu, L. Xing, P.-A. Heng. Transformation-consistent self-ensembling model for semisupervised medical image segmentation. IEEE Trans. Neural Netw. Learn., 32, 523-534(2020).
