Prugger C, Keil U, Wellmann J, De Bacquer D, Backer G, Ambrosio GB, Reiner Z, Gaita D, Wood D, Kotseva K et al (2011) Blood pressure control and knowledge of target blood pressure in coronary patients across Europe: results from the euroaspire iii survey. J Hypertens 29(8):1641–1648

CAS  PubMed  Google Scholar 

Krum H, Schlaich M, Whitbourn R, Sobotka PA, Sadowski J, Bartus K, Kapelak B, Walton A, Sievert H, Thambar S et al (2009) Catheter-based renal sympathetic denervation for resistant hypertension: a multicentre safety and proof-of-principle cohort study. Lancet 373(9671):1275–1281

PubMed  Google Scholar 

Marin F, Fezzi S, Gambaro A, Ederle F, Castaldi G, Widmann M, Gangemi C, Ferrero V, Pesarini G, Pighi M, et al (2021) Insights on safety and efficacy of renal artery denervation for uncontrolled-resistant hypertension in a high risk population with chronic kidney disease: first Italian real-world experience. Journal of Nephrology, 1–11

Fezzi S, Castaldi G, Widmann M, Ruzzarin A, Tavella D, Ribichini F (2022) Integrated anatomical and functional approach for tailored renal interventions-in patients with resistant arterial hypertension. J Nephrol 35(6):1747–1752

PubMed  Google Scholar 

Mahfoud F, Bhatt DL (2013) Catheter-based renal denervation: the black box procedure. American College of Cardiology Foundation Washington, DC

Mahfoud F, Böhm M, Azizi M, Pathak A, Durand Zaleski I, Ewen S, Tsioufis K, Andersson B, Blankestijn PJ, Burnier M et al (2015) Proceedings from the European clinical consensus conference for renal denervation: considerations on future clinical trial design. Eur Heart J 36(33):2219–2227

PubMed  Google Scholar 

Thukkani AK, Bhatt DL (2013) Renal denervation therapy for hypertension. Circulation 128(20):2251–2254

PubMed  Google Scholar 

Fezzi S, Castaldi G, Widmann M, Marin F, Ribichini F (2021) Il ritorno della denervazione renale nella cura dell’ipertensione arteriosa: nuove evidenze e indicazioni cliniche. G Ital Cardiol 22(3):17–24

Google Scholar 

Huang Y, Yang J, Sun Q, Ma S, Yuan Y, Tan W, Cao P, Feng C (2022) Vessel filtering and segmentation of coronary ct angiographic images. Int J Comput Assist Radiol Surg 17(10):1879–1890

PubMed  Google Scholar 

Li H, Tang Z, Nan Y, Yang G (2022) Human treelike tubular structure segmentation: a comprehensive review and future perspectives. Comput Biol Med 151:106241

PubMed  Google Scholar 

Zhang H, Gao Z, Zhang D, Hau WK, Zhang H (2022) Progressive perception learning for main coronary segmentation in x-ray angiography. IEEE Trans Med Imaging 42(3):864–879

Google Scholar 

Zhu R, Oda M, Hayashi Y, Kitasaka T, Misawa K, Fujiwara M, Mori K (2024) Skeleton-guided 3d convolutional neural network for tubular structure segmentation. Int J Comput Assisted Radiol Surg 1–11

Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: proceedings of the european conference on computer vision (ECCV), pp. 801–818

Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203–211

CAS  PubMed  Google Scholar 

Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D(2021) Swin unetr: swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 . Springer

Hao D, Ding S, Qiu L, Lv Y, Fei B, Zhu Y, Qin B (2020) Sequential vessel segmentation via deep channel attention network. Neural Netw 128:172–187

PubMed  PubMed Central  Google Scholar 

Chen C, Wang G, Peng C, Fang Y, Zhang D, Qin H (2021) Exploring rich and efficient spatial temporal interactions for real-time video salient object detection. IEEE Trans Image Process 30:3995–4007

PubMed  Google Scholar 

Teed Z, Deng J (2020) Raft: recurrent all-pairs field transforms for optical flow. In: computer vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, pp. 402–419 . Springer

Wu CH, Chen SH, Hu CY, Wu HY, Chen KH, Chen YY, Su CH, Lee CK, Liu YL (2025) Denver: Deformable neural vessel representations for unsupervised video vessel segmentation. In: proceedings of the computer vision and pattern recognition conference, pp. 15682–15692

Bai W, Suzuki H, Qin C, Tarroni G, Oktay O, Matthews PM, Rueckert D (2018) Recurrent neural networks for aortic image sequence segmentation with sparse annotations. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st international conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV 11, pp. 586–594. Springer

Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

Heo M, Hwang S, Oh SW, Lee J-Y, Kim SJ (2022) Vita: video instance segmentation via object token association. Adv Neural Inf Process Syst 35:23109–23120

Google Scholar 

Liu Z, Ning J, Cao Y, Wei Y, Zhang Z, Lin S, Hu H (2022) Video swin transformer. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3202–3211

Zhang Y, Borse S, Cai H, Porikli F (2022) AuxAdapt: stable and efficient test-time adaptation for temporally consistent video semantic segmentation. In: 2022 IEEE/CVF winter conference on applications of computer vision (WACV), pp. 2633–2642. IEEE, Waikoloa, HI, USA

Wei H, Cao H, Cao Y, Zhou Y, Xue W, Ni D, Li S (2020) Temporal-consistent segmentation of echocardiography with co-learning from appearance and shape. In: medical image computing and computer assisted intervention–MICCAI 2020 vol. 12262, pp. 623–632. Springer, Cham

Varghese S, Gujamagadi S, Klingner M, Kapoor N, Bar A, Schneider JD, Maag K, Schlicht P, Huger F, Fingscheidt T (2021) An unsupervised temporal consistency (TC) loss to improve the performance of semantic segmentation networks. In: 2021 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp. 12–20. IEEE, Nashville, TN, USA

Zhang Y, Borse S, Cai H, Wang Y, Bi N, Jiang X, Porikli F (2022) Perceptual consistency in video segmentation. In: 2022 IEEE/CVF winter conference on applications of computer vision (WACV), pp. 2623–2632. IEEE, Waikoloa, HI, USA

Grammatikopoulou M, Sanchez-Matilla R, Bragman F, Owen D, Culshaw L, Kerr K, Stoyanov D, Luengo I (2024) A spatio-temporal network for video semantic segmentation in surgical videos. Int J Comput Assist Radiol Surg 19(2):375–382

PubMed  Google Scholar 

Zhang T, Tian X, Wu Y, Ji S, Wang X, Zhang Y, Wan P (2023) Dvis: Decoupled video instance segmentation framework. In: proceedings of the IEEE/CVF international conference on computer vision, pp. 1282–1291

Liao B, Chen S, Zhang Y, Jiang B, Zhang Q, Liu W, Huang C, Wang X (2023) MapTRv2: an end-to-end framework for online vectorized HD map construction. arXiv

Johnson KR, Patel SJ, Whigham A, Hakim A, Pettigrew RI, Oshinski JN (2004) Three-dimensional, time-resolved motion of the coronary arteries. J Cardiovasc Magn Reson 6(3):663–673

PubMed  Google Scholar 

Lin H, Wu R, Liu S, Lu J, Jia J (2021) Video instance segmentation with a propose-reduce paradigm. In: 2021 IEEE/CVF international conference on computer vision (ICCV), pp. 1719–1728. IEEE, Montreal, QC, Canada

Cheng B, Choudhuri A, Misra I, Kirillov A, Girdhar R, Schwing AG (2021) Mask2former for video instance segmentation. arXiv