Transformer and convolutional based dual branch network for retinal vessel segmentation in OCTA images

Wu, 2021, SCS-Net: A scale and context sensitive network for retinal vessel segmentation, Med. Image Anal., 70, 10.1016/j.media.2021.102025 Tan, 2022, Retinal Vessel Segmentation with Skeletal Prior and Contrastive Loss, IEEE Trans. Med. Imaging, 10.1109/TMI.2022.3161681 Lahme, 2018, Evaluation of ocular perfusion in Alzheimer’s disease using optical coherence tomography angiography, J. Alzheimers Dis., 66, 1745, 10.3233/JAD-180738 Engerman, 1989, Pathogenesis of diabetic retinopathy, Diabetes, 38, 1203, 10.2337/diab.38.10.1203 Wei, 2021, Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm, IEEE Trans. Med. Imaging Hubbard, 1999, Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study, Ophthalmology, 106, 2269, 10.1016/S0161-6420(99)90525-0 Benson, 1978, Fluorescence properties of indocyanine green as related to angiography, Phys. Med. Biol., 23, 159, 10.1088/0031-9155/23/1/017 Li, 2020, Image projection network: 3D to 2D image segmentation in OCTA images, IEEE Trans. Med. Imaging, 39, 3343, 10.1109/TMI.2020.2992244 De Carlo, 2015, A review of optical coherence tomography angiography (OCTA), Int. J. Retina Vitreous, 1, 1, 10.1186/s40942-015-0005-8 Ma, 2020, ROSE: a retinal OCT-angiography vessel segmentation dataset and new model, IEEE Trans. Med. Imaging, 40, 928, 10.1109/TMI.2020.3042802 Liu, 2022, Weakly supervised segmentation of covid19 infection with scribble annotation on ct images, Pattern Recogn., 122, 10.1016/j.patcog.2021.108341 Liu, 2023, ResDO-UNet: A deep residual network for accurate retinal vessel segmentation from fundus images, Biomed. Signal Process. Control, 79, 10.1016/j.bspc.2022.104087 Liu, 2022, Scribble-Supervised Meibomian Glands Segmentation in Infrared Images, ACM Trans. Multimedia Comput., Commun., Appl. (TOMM), 18, 1, 10.1145/3497747 Pissas, 2020, Deep iterative vessel segmentation in OCT angiography, Biomed. Opt. Express, 11, 2490, 10.1364/BOE.384919 Z. Peng et al., Conformer: Local features coupling global representations for visual recognition, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 367–376. A. Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, in: International Conference on Learning Representations, 2020. Wang, 2020, Axial-deeplab: Stand-alone axial-attention for panoptic segmentation, 108 Yu, 2023, M3U-CDVAE: Lightweight retinal vessel segmentation and refinement network, Biomed. Signal Process. Control, 79, 10.1016/j.bspc.2022.104113 Wu, 2021, PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation, 1579 Deng, 2022, A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network, Biomed. Signal Process. Control, 73, 10.1016/j.bspc.2021.103467 Mou, 2019, CS-Net: channel and spatial attention network for curvilinear structure segmentation, 721 Guo, 2021, An end-to-end network for segmenting the vasculature of three retinal capillary plexuses from OCT angiographic volumes, Biomed. Opt. Express, 12, 4889, 10.1364/BOE.431888 Chen, 2022, Dual-consistency semi-supervision combined with self-supervision for vessel segmentation in retinal OCTA images, Biomed. Opt. Express, 13, 2824, 10.1364/BOE.458004 Kirillov, 2020, PointRend: Image Segmentation As Rendering, 9796 Liu, 2021, Swin transformer: Hierarchical vision transformer using shifted windows, 10012 Valanarasu, 2021, Medical transformer: Gated axial-attention for medical image segmentation, 36 He, 2016, Deep Residual Learning for Image Recognition, 770 Antony, 2011, Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images, Biomed. Opt. Express, 2, 2403, 10.1364/BOE.2.002403 Garvin, 2009, Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images, IEEE Trans. Med. Imaging, 28, 1436, 10.1109/TMI.2009.2016958 Li, 2005, Optimal surface segmentation in volumetric images-a graph-theoretic approach, IEEE Trans. Pattern Anal. Mach. Intell., 28, 119 Zhang, 2020, Robust layer segmentation against complex retinal abnormalities for en face OCTA generation, 647 Xiao, 2018, Weighted res-unet for high-quality retina vessel segmentation, 327 Peng, 2021, Fargo: A joint framework for faz and rv segmentation from octa images, 42 Ronneberger, 2015, U-net: Convolutional networks for biomedical image segmentation, 234 H. Cao et al., Swin-unet: Unet-like pure transformer for medical image segmentation, arXiv preprint arXiv:2105.05537, 2021. J. Chen et al., Transunet: Transformers make strong encoders for medical image segmentation, arXiv preprint arXiv:2102.04306, 2021. Ri, 2020, Extreme learning machine with hybrid cost function of G-mean and probability for imbalance learning, Int. J. Mach. Learn. Cybern., 11, 2007, 10.1007/s13042-020-01090-x Sathananthavathi, 2021, Encoder enhanced atrous (EEA) unet architecture for retinal blood vessel segmentation, Cogn. Syst. Res., 67, 84, 10.1016/j.cogsys.2021.01.003 Shi, 2021, MD-Net: A multi-scale dense network for retinal vessel segmentation, Biomed. Signal Process. Control, 70, 10.1016/j.bspc.2021.102977 Guo, 2021, Sa-unet: Spatial attention u-net for retinal vessel segmentation, 1236 Liu, 2020, Confidence-guided topology-preserving layer segmentation for optical coherence tomography images with focus-column module, IEEE Trans. Instrum. Meas., 70, 1 Guo, 2022, CSGNet: Cascade semantic guided net for retinal vessel segmentation, Biomed. Signal Process. Control, 78, 10.1016/j.bspc.2022.103930 Liu, 2018, Automated layer segmentation of retinal optical coherence tomography images using a deep feature enhanced structured random forests classifier, IEEE J. Biomed. Health Inform., 23, 1404, 10.1109/JBHI.2018.2856276