[1] Su H, Kwok K W, Cleary K et al. State of the art and future opportunities in MRI-guided robot-assisted surgery and interventions[J]. Proceedings of the IEEE, 110, 968-992(2022).

[2] Forner A, Reig M, Bruix J. Hepatocellular carcinoma[J]. Lancet, 391, 1301-1314(2018).

[3] Wright J. Surgery: the eyes of the operation[J]. Nature, 502, S88-S89(2013).

[4] Kang T W, Rhim H. Recent advances in tumor ablation for hepatocellular carcinoma[J]. Liver Cancer, 4, 176-187(2015).

[5] Zhu M L, Chen Z, Yuan Y X. DSI-net: deep synergistic interaction network for joint classification and segmentation with endoscope images[J]. IEEE Transactions on Medical Imaging, 40, 3315-3325(2021).

[6] Ji X, Feng M, Treb K et al. Development of an integrated C-arm interventional imaging system with a strip photon counting detector and a flat panel detector[J]. IEEE Transactions on Medical Imaging, 40, 3674-3685(2021).

[7] Hu J, Luo Z W, Wang X et al. End-to-end multimodal image registration via reinforcement learning[J]. Medical Image Analysis, 68, 101878(2021).

[8] Kochanski R B, Lombardi J M, Laratta J L et al. Image-guided navigation and robotics in spine surgery[J]. Neurosurgery, 84, 1179-1189(2019).

[9] Tao C N, Zheng Z R. Augmented reality computational spectral imaging for surgical guidance[J]. Laser & Optoelectronics Progress, 59, 2011014(2022).

[10] Zhu J J, Li H, Ai D N et al. Iterative closest graph matching for non-rigid 3D/2D coronary arteries registration[J]. Computer Methods and Programs in Biomedicine, 199, 105901(2021).

[11] Ambrosini P, Smal I, Ruijters D et al. A hidden Markov model for 3D catheter tip tracking with 2D X-ray catheterization sequence and 3D rotational angiography[J]. IEEE Transactions on Medical Imaging, 36, 757-768(2017).

[12] Matl S, Brosig R, Baust M et al. Vascular image registration techniques: a living review[J]. Medical Image Analysis, 35, 1-17(2017).

[13] Rodríguez E, Kypson A P, Moten S C et al. Robotic mitral surgery at East Carolina University: a 6 year experience[J]. The International Journal of Medical Robotics and Computer Assisted Surgery, 2, 211-215(2006).

[14] Ishizawa T, Fukushima N, Shibahara J et al. Real-time identification of liver cancers by using indocyanine green fluorescent imaging[J]. Cancer, 115, 2491-2504(2009).

[15] Miyata A, Ishizawa T, Tani K et al. Reappraisal of a dye-staining technique for anatomic hepatectomy by the concomitant use of indocyanine green fluorescence imaging[J]. Journal of the American College of Surgeons, 221, e27-e36(2015).

[16] Chen D F, Ren J, Wang Y et al. Intraoperative monitoring of blood perfusion in port wine stains by laser Doppler imaging during vascular targeted photodynamic therapy: a preliminary study[J]. Photodiagnosis and Photodynamic Therapy, 14, 142-151(2016).

[17] Wang Y, Xu Y X, Guo X H et al. Enhanced antimicrobial activity through the combination of antimicrobial photodynamic therapy and low-frequency ultrasonic irradiation[J]. Advanced Drug Delivery Reviews, 183, 114168(2022).

[18] Herregodts S, Verhaeghe M, De Coninck B et al. An improved method for assessing the technical accuracy of optical tracking systems for orthopaedic surgical navigation[J]. The International Journal of Medical Robotics and Computer Assisted Surgery, 17, e2285(2021).

[19] Burström G, Nachabe R, Homan R et al. Frameless patient tracking with adhesive optical skin markers for augmented reality surgical navigation in spine surgery[J]. Spine, 45, 1598-1604(2020).

[20] Saeidi H, Ge J, Kam M et al. Supervised autonomous electrosurgery via biocompatible near-infrared tissue tracking techniques[J]. IEEE Transactions on Medical Robotics and Bionics, 1, 228-236(2019).

[21] Sheinker A, Moldwin M B. Adaptive interference cancelation using a pair of magnetometers[J]. IEEE Transactions on Aerospace and Electronic Systems, 52, 307-318(2016).

[22] Marmulla R, Lüth T, Mühling J et al. Markerless laser registration in image-guided oral and maxillofacial surgery[J]. Journal of Oral and Maxillofacial Surgery, 62, 845-851(2004).

[23] Ledderose G J, Stelter K, Leunig A et al. Surface laser registration in ENT-surgery: accuracy in the paranasal sinuses-a cadaveric study[J]. Rhinology, 45, 281-285(2007).

[24] Marmulla R, Eggers G, Mühling J. Laser surface registration for lateral skull base surgery[J]. Min-Minimally Invasive Neurosurgery, 48, 181-185(2005).

[25] Malham G M, Munday N R. Comparison of novel machine vision spinal image guidance system with existing 3D fluoroscopy-based navigation system: a randomized prospective study[J]. The Spine Journal, 22, 561-569(2022).

[26] Li W J, Fan J F, Li S W et al. Calibrating 3D scanner in the coordinate system of optical tracker for image-to-patient registration[J]. Frontiers in Neurorobotics, 15, 636772(2021).

[27] Chen L, Zhang F F, Zhan W et al. Research on the accuracy of three-dimensional localization and navigation in robot-assisted spine surgery[J]. The International Journal of Medical Robotics and Computer Assisted Surgery, 16, e2071(2020).

[28] Swank M L, Alkire M, Conditt M et al. Technology and cost-effectiveness in knee arthroplasty: computer navigation and robotics[J]. American Journal of Orthopedics, 38, 32-36(2009).

[29] Zhou G, Chen X Q, Niu B L et al. Intraoperative localization of small pulmonary nodules to assist surgical resection: a novel approach using a surgical navigation puncture robot system[J]. Thoracic Cancer, 11, 72-81(2020).

[30] Bonatti J, Vetrovec G, Riga C L et al. Robotic technology in cardiovascular medicine[J]. Nature Reviews Cardiology, 11, 266-275(2014).

[31] Jeon S, Hoshiar A K, Kim K et al. A magnetically controlled soft microrobot steering a guidewire in a three-dimensional phantom vascular network[J]. Soft Robotics, 6, 54-68(2019).

[32] Kim Y, Parada G A, Liu S D et al. Ferromagnetic soft continuum robots[J]. Science Robotics, 4, eaax7329(2019).

[33] Jeong S, Choi H, Go G et al. Feasibility study on magnetically steerable guidewire device for percutaneous coronary intervention[J]. International Journal of Control, Automation and Systems, 15, 473-479(2017).

[34] Zhang S X, Yin M, Lai Z Y et al. Design and characteristics of 3D magnetically steerable guidewire system for minimally invasive surgery[J]. IEEE Robotics and Automation Letters, 7, 4040-4046(2022).

[35] Kyriakides Y. Accuracy assessment of a novel optical image guided system for trans-nasal sinus and skull base surgeries[J]. International Bulletin of Otorhinolaryngology, 16, 41(2020).

[36] Trope M, Shamir R R, Joskowicz L et al. The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery[J]. International Journal of Computer Assisted Radiology and Surgery, 10, 1127-1140(2015).

[37] Cong W J, Yang J A, Ai D N et al. Quantitative analysis of deformable model based 3-D reconstruction of coronary artery from multiple angiograms[J]. IEEE Transactions on Biomedical Engineering, 62, 2079-2090(2015).

[38] Seitel A, Engel M, Sommer C M et al. Computer-assisted trajectory planning for percutaneous needle insertions[J]. Medical Physics, 38, 3246-3259(2011).

[39] Fu T Y, Yang J, Li Q et al. Groupwise registration with global-local graph shrinkage in atlas construction[J]. Medical Image Analysis, 64, 101711(2020).

[40] Song S, Yang J, Ai D N et al. Patch-based adaptive background subtraction for vascular enhancement in X-ray cineangiograms[J]. IEEE Journal of Biomedical and Health Informatics, 23, 2563-2575(2019).

[41] Azzopardi G, Strisciuglio N, Vento M et al. Trainable COSFIRE filters for vessel delineation with application to retinal images[J]. Medical Image Analysis, 19, 46-57(2015).

[42] Li Y L, Zhou S J, Wu J H et al. A novel method of vessel segmentation for X-ray coronary angiography images[C], 468-471(2012).

[43] Hernandez-Vela A, Gatta C, Escalera S et al. Accurate coronary centerline extraction, caliber estimation, and catheter detection in angiographies[J]. IEEE Transactions on Information Technology in Biomedicine, 16, 1332-1340(2012).

[44] Liu T, Tai Y H, Zhao C M et al. Augmented reality in neurosurgical navigation: a survey[J]. The International Journal of Medical Robotics and Computer Assisted Surgery, 16, 1-20(2020).

[45] Minaee S, Minaei M, Abdolrashidi A. Deep-emotion: facial expression recognition using attentional convolutional network[J]. Sensors, 21, 3046(2021).

[46] Chen L C, Papandreou G, Kokkinos I et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J]. arXiv preprint(2014).

[47] Wang J C, Shu Y C, Lin C Y et al. Application of deep learning algorithms in automatic sonographic localization and segmentation of the Median nerve: a systematic review and meta-analysis[J]. Artificial Intelligence in Medicine, 137, 102496(2023).

[48] Alqazzaz S, Sun X, Yang X et al. Automated brain tumor segmentation on multi-modal MR image using SegNet[J]. Computer Vision and Image Understanding, 5, 209-219(2019).

[49] Christ P F, Elshaer M E A, Ettlinger F et al. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields[M]. Ourselin S, Joskowicz L, Sabuncu M R, et al. Medical image computing and computer-assisted intervention-MICCAI 2016. Lecture notes in computer science, 9901, 415-423(2016).

[50] 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).

[51] 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).

[52] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[53] Isensee F, Jaeger P F, Kohl S A A et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nature Methods, 18, 203-211(2021).

[54] Zhou Z W, Siddiquee M M R, Tajbakhsh N et al. UNet++: a nested U-net architecture for medical image segmentation[M]. Stoyanov D, Taylor Z, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science, 11045, 3-11(2018).

[55] Alamir M, Alghamdi M. The role of generative adversarial network in medical image analysis: an In-depth survey[J]. ACM Computing Surveys, 55, 96(2022).

[56] Xue Y, Xu T, Zhang H et al. SegAN: adversarial network with multi-scale L1 loss for medical image segmentation[J]. Neuroinformatics, 16, 383-392(2018).

[57] Dai W, Dong N Q, Wang Z Y et al. SCAN: structure correcting adversarial network for organ segmentation in chest X-rays[M]. Stoyanov D, Taylor Z, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science, 11045, 263-273(2018).

[58] Dai Z Y, Yang R Q, Hang F et al. Neurosurgical craniotomy localization using interactive 3D lesion mapping for image-guided neurosurgery[J]. IEEE Access, 7, 10606-10616(2019).

[59] Hu W R, Jiang H Y, Wang M. Flexible needle puncture path planning for liver tumors based on deep reinforcement learning[J]. Physics in Medicine & Biology, 67, 195008(2022).

[60] Luo M, Jiang H Y, Shi T Y. Multi-stage puncture path planning algorithm of ablation needles for percutaneous radiofrequency ablation of liver tumors[J]. Computers in Biology and Medicine, 145, 105506(2022).

[61] Shatrov J, Parker D. Computer and robotic-assisted total knee arthroplasty: a review of outcomes[J]. Journal of Experimental Orthopaedics, 7, 70(2020).

[62] Hafez M A, Chelule K L, Seedhom B B et al. Computer-assisted total knee arthroplasty using patient-specific templating[J]. Clinical Orthopaedics & Related Research, 444, 184-192(2006).

[63] Xiao D Q, Wang L, Deng H et al. Estimating reference bony shape model for personalized surgical reconstruction of posttraumatic facial defects[M]. Shen D G, Liu T M, Peters T M, et al. Medical image computing and computer assisted intervention-MICCAI 2019. Lecture notes in computer science, 11768, 327-335(2019).

[64] Xiao D Q, Deng H, Lian C F et al. Unsupervised learning of reference bony shapes for orthognathic surgical planning with a surface deformation network[J]. Medical Physics, 48, 7735-7746(2021).

[65] Xiao D Q, Lian C F, Deng H et al. Estimating reference bony shape models for orthognathic surgical planning using 3D point-cloud deep learning[J]. IEEE Journal of Biomedical and Health Informatics, 25, 2958-2966(2021).

[66] Liu S Y, Fan J F, Song D P et al. Joint estimation of depth and motion from a monocular endoscopy image sequence using a multi-loss rebalancing network[J]. Biomedical Optics Express, 13, 2707(2022).

[67] Yu T, Zheng S A, Zhang X W et al. A novel computer navigation method for accurate percutaneous sacroiliac screw implantation[J]. Medicine, 98, e14548(2019).

[68] Gupta S, Gupta P, Verma V S. Study on anatomical and functional medical image registration methods[J]. Neurocomputing, 452, 534-548(2021).

[69] Fan J F, Yang J, Zhao Y T et al. Convex hull aided registration method (CHARM)[J]. IEEE Transactions on Visualization and Computer Graphics, 23, 2042-2055(2017).

[70] Sengupta D, Gupta P, Biswas A. A survey on mutual information based medical image registration algorithms[J]. Neurocomputing, 486, 174-188(2022).

[71] Chu Y K, Yang J, Ma S D et al. Registration and fusion quantification of augmented reality based nasal endoscopic surgery[J]. Medical Image Analysis, 42, 241-256(2017).

[72] Giesel F L, Mehndiratta A, Locklin J et al. Image fusion using CT, MRI and PET for treatment planning, navigation and follow up in percutaneous RFA[J]. Experimental Oncology, 31, 106-114(2009).

[73] Sundar H, Shen D G, Biros G et al. Robust computation of mutual information using spatially adaptive meshes[M]. Ayache N, Ourselin S, Maeder A. Medical image computing and computer-assisted intervention-MICCAI 2007. Lecture notes in computer science, 4791, 950-958(2007).

[74] Fu T Y, Fan J F, Liu D K et al. Divergence-free fitting-based incompressible deformation quantification of liver[J]. IEEE Journal of Biomedical and Health Informatics, 25, 720-736(2021).

[75] Sentker T, Madesta F, Werner R. GDL-FIRE4D: deep learning-based fast 4D CT image registration[M]. Frangi A F, Schnabel J A, Davatzikos C, et al. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2018. Lecture notes in computer science, 11070, 765-773(2018).

[76] Uzunova H, Wilms M, Handels H et al. Training CNNs for image registration from few samples with model-based data augmentation[M]. Descoteaux M, Maier-Hein L, Franz A, et al. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2017. Lecture notes in computer science, 10433, 223-231(2017).

[77] Cheng X, Zhang L, Zheng Y F. Deep similarity learning for multimodal medical images[J]. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 6, 248-252(2018).

[78] Du B, Liao J D, Turkbey B et al. Multi-task learning for registering images with large deformation[J]. IEEE Journal of Biomedical and Health Informatics, 25, 1624-1633(2021).

[79] Fan J F, Cao X H, Yap P T et al. BIRNet: brain image registration using dual-supervised fully convolutional networks[J]. Medical Image Analysis, 54, 193-206(2019).

[80] Blendowski M, Bouteldja N, Heinrich M P. Multimodal 3D medical image registration guided by shape encoder-decoder networks[J]. International Journal of Computer Assisted Radiology and Surgery, 15, 269-276(2020).

[81] Sedghi A, Luo J, Mehrtash A et al. Semi-supervised image registration using deep learning[J]. Proceedings of SPIE, 10951, 109511G(2019).

[82] Yoo I, Hildebrand D G C, Tobin W F et al. ssEMnet: serial-section electron microscopy image registration using a spatial transformer network with learned features[M]. Cardoso M J, Arbel T, Carneiro G, et al. Deep learning in medical image analysis, international workshop on multimodal learning for clinical decision support. Lecture notes in computer science, 10553, 249-257(2017).

[83] Lin L H, Yi J B, Cao F et al. Non-rigid registration algorithm of lung computed tomography images based on multi-scale parallel full convolution neural network[J]. Laser & Optoelectronics Progress, 59, 1617004(2022).

[84] Zhu J J, Fan J F, Guo S A et al. Heuristic tree searching for pose-independent 3D/2D rigid registration of vessel structures[J]. Physics in Medicine & Biology, 65, 055010(2020).

[85] Jeroen V H, Emmanuel A, Zheng G Y et al. Deep learning-based 2D/3D registration of an atlas to biplanar X-ray images[J]. International Journal of Computer Assisted Radiology and Surgery, 17, 1333-1342(2022).

[86] Miao S, Wang Z J, Liao R. A CNN regression approach for real-time 2D/3D registration[J]. IEEE Transactions on Medical Imaging, 35, 1352-1363(2016).

[87] Vijayan R, Sheth N, Mekki L et al. 3D–2D image registration in the presence of soft-tissue deformation in image-guided transbronchial interventions[J]. Physics in Medicine & Biology, 68, 015010(2023).

[88] Varnavas A, Carrell T, Penney G. Fully automated 2D-3D registration and verification[J]. Medical Image Analysis, 26, 108-119(2015).

[89] Flach B, Brehm M, Sawall S et al. Deformable 3D–2D registration for CT and its application to low dose tomographic fluoroscopy[J]. Physics in Medicine and Biology, 59, 7865-7887(2014).

[90] Li W J, Kong D Q, Cao G G et al. 2D-3D medical image registration based on training-push understanding coupling architecture[J]. Laser & Optoelectronics Progress, 59, 1610015(2022).

[91] Wohlhart P, Lepetit V. Learning descriptors for object recognition and 3D pose estimation[C], 3109-3118(2015).

[92] Heinrich M P, Jenkinson M, Bhushan M et al. MIND: modality independent neighbourhood descriptor for multi-modal deformable registration[J]. Medical Image Analysis, 16, 1423-1435(2012).

[93] Heinrich M P, Jenkinson M, Papież B W et al. Towards realtime multimodal fusion for image-guided interventions using self-similarities[M]. International conference on medical image computing and computer-assisted intervention-MICCAI 2013. Lecture notes in computer science, 8149, 187-194(2013).

[94] Wang Y F, Fu T Y, Wu C et al. Multimodal registration of ultrasound and MR images using weighted self-similarity structure vector[J]. Computers in Biology and Medicine, 155, 106661(2023).

[95] Wei W, Haishan X, Alpers J et al. A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation[J]. Computer Methods and Programs in Biomedicine, 206, 106117(2021).

[96] Markova V, Ronchetti M, Wein W et al. Global multi-modal 2D/3D registration via local descriptors learning[M]. Wang L W, Dou Q, Fletcher P T, et al. Medical image computing and computer-assisted intervention-MICCAI 2022. Lecture notes in computer science, 13436, 269-279(2022).

[97] Dey D, Slomka P J, Gobbi D G et al. Mixed reality merging of endoscopic images and 3-D surfaces[M]. Delp S L, DiGoia A M, Jaramaz B. Medical image computing and computer-assisted intervention-MICCAI 2000. Lecture notes in computer science, 1935, 796-803(2000).

[98] Li W J, Fan J F, Li S W et al. Homography-based robust pose compensation and fusion imaging for augmented reality based endoscopic navigation system[J]. Computers in Biology and Medicine, 138, 104864(2021).

[99] Suciadi L P, William Y, Jorizal P et al. Comparing lung CT in COVID-19 pneumonia and acute heart failure: an imaging conundrum[J]. Cureus, 13, e15120(2021).

[100] Behrens A, Guski M, Stehle T et al. A non-linear multi-scale blending algorithm for fluorescence bladder images[J]. Computer Science-Research and Development, 26, 125-134(2011).

[101] Tan W, Tiwari P, Pandey H M et al. Multimodal medical image fusion algorithm in the era of big data[J]. Neural Computing and Applications, 1-21(2020).

[102] Higgins W E, Helferty J P, Lu K K et al. 3D CT-video fusion for image-guided bronchoscopy[J]. Computerized Medical Imaging and Graphics, 32, 159-173(2008).

[103] Chu Y K, Li X, Yang X L et al. Perception enhancement using importance-driven hybrid rendering for augmented reality based endoscopic surgical navigation[J]. Biomedical Optics Express, 9, 5205-5226(2018).

[104] Tarsitano A, Ricotta F, Baldino G et al. Navigation-guided resection of maxillary tumours: the accuracy of computer-assisted surgery in terms of control of resection margins-a feasibility study[J]. Journal of Cranio-Maxillofacial Surgery, 45, 2109-2114(2017).

[105] Bolding S L, Reebye U N. Accuracy of haptic robotic guidance of dental implant surgery for completely edentulous arches[J]. The Journal of Prosthetic Dentistry, 128, 639-647(2022).

[106] Bai S Z, Ren N, Feng Z H et al. Animal experiment on the accuracy of the Autonomous Dental Implant Robotic System[J]. Chinese Journal of Stomatology, 56, 170-174(2021).

[107] Mathew K K, Marchand K B, Tarazi J M et al. Computer-assisted navigation in total knee arthroplasty[J]. Surgical Technology International, 36, 323-330(2020).

[108] Jones C W, Jerabek S A. Current role of computer navigation in total knee arthroplasty[J]. The Journal of Arthroplasty, 33, 1989-1993(2018).

[109] Shimokawa N, Takami T. Surgical safety of cervical pedicle screw placement with computer navigation system[J]. Neurosurgical Review, 40, 251-258(2017).

[110] D'Souza M, Gendreau J, Feng A et al. Robotic-assisted spine surgery: history, efficacy, cost, and future trends[J]. Robotic Surgery (Auckland), 6, 9-23(2019).

[111] Balboni J M, Siddique K, Nomoto E K et al. Novel use of robotics and navigation for anterior lumbar total disc replacement surgery[J]. North American Spine Society Journal, 9, 100097(2022).

[112] Hong J, Nakashima H, Konishi K et al. Interventional navigation for abdominal therapy based on simultaneous use of MRI and ultrasound[J]. Medical and Biological Engineering and Computing, 44, 1127-1134(2006).

[113] Krücker J, Xu S, Glossop N et al. Electromagnetic tracking for thermal ablation and biopsy guidance: clinical evaluation of spatial accuracy[J]. Journal of Vascular and Interventional Radiology, 18, 1141-1150(2007).

[114] Zhou Y J, Xie X L, Zhou X H et al. Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy[J]. Computerized Medical Imaging and Graphics, 83, 101734(2020).

[115] Ma H, Smal I, Daemen J et al. Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering[J]. Medical Image Analysis, 61, 101634(2020).