[1] Kochanek K D, Murphy S L, Xu J Q et al. Deaths: final data for 2017[J]. National Vital Statistics Reports, 68, 1-77(2019).

[2] Wei Y Z, Yuan X, Lan G P et al. Research progress and application of cardiovascular optical coherence tomography[J]. Laser & Optoelectronics Progress, 58, 2400002(2021).

[3] Tian F, Zhou S S, Liu J H et al. Treatment of severely calcified coronary artery disease by intravascular lithotripsy primary outcomes and 180-day follow-up from the Chinese SOLSTICE Trial[J]. Journal of Geriatric Cardiology, 20, 32-39(2023).

[4] Jiang W H, Zhang Z M, Li Y D et al. The cell origin and role of osteoclastogenesis and osteoblastogenesis in vascular calcification[J]. Frontiers in Cardiovascular Medicine, 8, 639740(2021).

[5] Zheng S, Fei Y, Jian S. Method for parametric imaging of attenuation by intravascular optical coherence tomography[J]. Biomedical Optics Express, 12, 1882-1904(2021).

[6] Han Z J, Lin X K, Qiu Y Y et al. Identification and risk assessment of atherosclerotic plaques based on IVOCT[J]. Chinese Journal of Lasers, 51, 0907017(2024).

[7] Tearney G J, Regar E, Akasaka T et al. Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the International Working Group for Intravascular Optical Coherence Tomography Standardization and Validation[J]. Journal of the American College of Cardiology, 59, 1058-1072(2012).

[8] Sun Z, Wang S Y. Application of deep learning in intravascular optical coherence tomography[J]. Laser & Optoelectronics Progress, 59, 2200002(2022).

[9] Lu H, Gargesha M, Wang Z et al. Automatic stent detection in intravascular OCT images using bagged decision trees[J]. Biomedical Optics Express, 3, 2809-2824(2012).

[10] Rico-Jimenez J J, Campos-Delgado D U, Villiger M et al. Automatic classification of atherosclerotic plaques imaged with intravascular OCT[J]. Biomedical Optics Express, 7, 4069-4085(2016).

[11] Yang J L, Zhang B, Wang H R et al. Automated characterization and classification of coronary atherosclerotic plaques for intravascular optical coherence tomography[J]. Biocybernetics and Biomedical Engineering, 39, 719-727(2019).

[12] Gharaibeh Y, Prabhu D, Kolluru C et al. Coronary calcification segmentation in intravascular OCT images using deep learning: application to calcification scoring[J]. Journal of Medical Imaging, 6, 045002(2019).

[13] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

[14] Lee J, Prabhu D, Kolluru C et al. Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features[J]. Scientific Reports, 10, 2596(2020).

[15] Sun H Y, Zhao C, Qin Y H et al. In vivo detection of plaque erosion by intravascular optical coherence tomography using artificial intelligence[J]. Biomedical Optics Express, 13, 3922-3938(2022).

[16] Wang Z, Zheng J W, Jiang P L et al. Sk-Conv and SPP-based UNet for lesion segmentation of coronary optical coherence tomography[J]. Technology and Health Care, 31, 347-355(2023).

[17] Li C, Jia H B, Tian J W et al. Comprehensive assessment of coronary calcification in intravascular OCT using a spatial-temporal encoder-decoder network[J]. IEEE Transactions on Medical Imaging, 41, 857-868(2022).

[18] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

[19] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

[20] Chen J N, Lu Y Y, Yu Q H et al. TransUNet: transformers make strong encoders for medical image segmentation[EB/OL]. http:∥arxiv.org/abs/2102.04306v1

[21] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

[22] Tan M X, Pang R M, Le Q V. EfficientDet: scalable and efficient object detection[C], 10778-10787(2020).

[23] Hu H, Zhang Z, Xie Z D et al. Local relation networks for image recognition[C], 3463-3472(2019).

[24] Ramachandran P, Parmar N, Vaswani A et al. Stand-alone self-attention in vision models[EB/OL]. https:∥arxiv.org/abs/1906.05909

[25] Zhao H S, Jia J Y, Koltun V. Exploring self-attention for image recognition[C], 10073-10082(2020).

[26] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017).

[27] Liu S, Qi L, Qin H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[28] Zhou S S, Tian F, Zhang T et al. Evaluation on the safety and accuracy of domestic optical coherence tomography system in clinical application[J]. Chinese Journal of Interventional Cardiology, 28, 199-202(2020).

[29] Tharwat A. Classification assessment methods[J]. Applied Computing and Informatics, 17, 168-192(2021).

[30] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

[31] Chen L C, Papandreou G, Schroff F et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. https:∥arxiv.org/abs/1706.05587

[32] Liu Z, Lin Y T, Cao Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2021).