[1] Schoenhagen P, Crowe T, Nicholls S, et al. IVUS Made Easy: An Introduction to Coronary Intravascular Ultrasound Imaging[M]. Great Britain: Informa Healthcare, 2008.
[4] Essa E, Xie X H, Sazonov I, et al. Automatic IVUS media-adventitia border extraction using double interface graph cut segmentation[C]//18th IEEE International Conference on Image Processing, Brussels, 2011: 69−72. https://doi.org/10.1109/ICIP.2011.6116649.
[6] Hernandez A H, Gil D G, Radeva P R, et al. Anisotropic processing of image structures for adventitia detection in intravascular ultrasound images[C]//Computers in Cardiology, 2004, Chicago, 2004: 229−232. https://doi.org/10.1109/CIC.2004.1442914.
[8] Mendizabal-Ruiz E G, Kakadiaris I A. Probabilistic segmentation of the lumen from intravascular ultrasound radio frequency data[C]//Proceedings of the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, Nice, 2012: 454–461. https://doi.org/10.1007/978-3-642-33418-4_56.
[9] Gil D, Radeva P, Saludes J, et al. Automatic segmentation of artery wall in coronary IVUS images: a probabilistic approach[C]//Computers in Cardiology 2000, Cambridge, 2000: 687–690. https://doi.org/10.1109/CIC.2000.898617.
[10] Gil D, Radeva P, Saludes J. Segmentation of artery wall in coronary IVUS images: a probabilistic approach[C]// Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, 2000: 352–355. https://doi.org/10.1109/ICPR.2000.902931.
[12] Yang J, Tong L, Faraji M, et al. IVUS-Net: an intravascular ultrasound segmentation network[C]//Proceedings of the 1st International Conference on Smart Multimedia, Toulon, 2018: 367–377. https://doi.org/10.1007/978-3-030-04375-9_31.
[15] Kim S, Jang Y, Jeon B, et al. Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images[C]//Proceedings of the 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, 2018: 161−168. https://doi.org/10.1007/978-3-030-01364-6_18.
[16] Xia M H, Yan W J, Huang Y, et al. Extracting membrane borders in IVUS images using a multi-scale feature aggregated U-Net[C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Montreal, 2020: 1650−1653. https://doi.org/10.1109/EMBC44109.2020.9175970.
[21] Sinha P, Wu Y M, Psaromiligkos I, et al. Lumen & media segmentation of IVUS images via ellipse fitting using a wavelet-decomposed subband CNN[C]//2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), Espoo, 2020: 1−6. https://doi.org/10.1109/MLSP49062.2020.9231871.
[22] Xie E Z, Sun P Z, Song X G, et al. PolarMask: single shot instance segmentation with polar representation[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 2020: 12190−12199. https://doi.org/10.1109/CVPR42600.2020.01221.
[23] Schmidt U, Weigert M, Broaddus C, et al. Cell detection with star-convex polygons[C]// Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, 2018: 265−273. https://doi.org/10.1007/978-3-030-00934-2_30.
[24] Zhang Z L, Zhang X Y, Peng C, et al. ExFuse: enhancing feature fusion for semantic segmentation[C]//Proceedings of the 15th European Conference on Computer Vision, Munich, 2018: 273–288. https://doi.org/10.1007/978-3-030-01249-6_17.
[25] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016: 770–778. https://doi.org/10.1109/CVPR.2016.90.
[26] Wang H H, Wu X D, Huang Z Y, et al. High-frequency component helps explain the generalization of convolutional neural networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 2020: 8681–8691. https://doi.org/10.1109/CVPR42600.2020.00871.
[28] Wyburd M K, Dinsdale N K, Namburete A I L, et al. TEDS-Net: enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations[C]//Proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, 2021: 250–260. https://doi.org/10.1007/978-3-030-87193-2_24.
[29] Chen L C, Zhu Y K, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the 15th European Conference on Computer Vision, Munich, 2018: 833851. https://doi.org/10.1007/978-3-030-01234-2_49.
[31] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[C]. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, 2015: 234241. https://doi.org/10.1007/978-3-319-24574-4_28.