{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:21:13Z","timestamp":1773325273927,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819570713","type":"print"},{"value":"9789819570720","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-7072-0_25","type":"book-chapter","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:09:13Z","timestamp":1773274153000},"page":"363-378","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FreezeSeg2RL: Frozen Segmentation Pretraining for\u00a0Reinforcement Learning On Vascular Interventional Robot Autonomous Delivering"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5487-661X","authenticated-orcid":false,"given":"Ziyang","family":"Mei","sequence":"first","affiliation":[]},{"given":"Siyuan","family":"Han","sequence":"additional","affiliation":[]},{"given":"Youchang","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Zitong","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Wenbo","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"25_CR1","unstructured":"Blomqvist, V.: Pymunk (2024). https:\/\/pymunk.org"},{"key":"25_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Chhikara, J., Goel, N., Rathee, N.: A critical analysis of transfer learning models for computer vision tasks. In: AIP Conference Proceedings, vol.\u00a03209. AIP Publishing (2024)","DOI":"10.1063\/5.0227772"},{"key":"25_CR4","doi-asserted-by":"publisher","unstructured":"Cho, Y., Park, J.H., Choi, J., Chang, D.E.: Sim-to-real transfer of image-based autonomous guidewire navigation trained by deep deterministic policy gradient with behavior cloning for fast learning. In: 2022 IEEE\/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), pp. 3468\u20133475. IEEE International Conference on Intelligent Robots and Systems, IEEE; Royal Soc Japan; IEEE Robot & Automat Soc; IES; SICE; New Technol Fdn (2022). https:\/\/doi.org\/10.1109\/IROS47612.2022.9982168, iEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23\u201327 October 2022","DOI":"10.1109\/IROS47612.2022.9982168"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Gomez-Paz, S., King, P., Gomez, A., Grandhi, R.: Exploring robotic advances, applications, and challenges in neuroendovascular surgery: a scoping review of the corpath GRX system. Intervent. Neuroradiol. 15910199241305691 (2024)","DOI":"10.1177\/15910199241305691"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Gruionu, L.G., et al.: Design and technical feasibility testing of a medical robot for flexible catheter navigation inside the lung airways. bioRxiv pp. 2024\u201305 (2024)","DOI":"10.1101\/2024.05.01.592024"},{"issue":"2","key":"25_CR7","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/s11548-021-02529-4","volume":"17","author":"S Jiang","year":"2021","unstructured":"Jiang, S., et al.: PixelTopoIS: a pixel-topology-coupled guidewire tip segmentation framework for robot-assisted intervention. Int. J. Comput. Assist. Radiol. Surg. 17(2), 329\u2013341 (2021). https:\/\/doi.org\/10.1007\/s11548-021-02529-4","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"11","key":"25_CR8","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1007\/s11548-022-02646-8","volume":"17","author":"L Karstensen","year":"2022","unstructured":"Karstensen, L., et al.: Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver. Int. J. Comput. Assist. Radiol. Surg. 17(11), 2033\u20132040 (2022)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Kweon, J., et al.: Deep reinforcement learning for guidewire navigation in coronary artery phantom. IEEE Access 9, 166409\u2013166422 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3135277","DOI":"10.1109\/ACCESS.2021.3135277"},{"key":"25_CR10","doi-asserted-by":"publisher","unstructured":"Li, N., Wang, Y., Cheng, H., Zhao, H., Ding, H.: Vascular centerline-guided autonomous navigation methods for robot-lead endovascular interventions. In: 2024 IEEE International Conference on Robotics and Automation (ICRA 2024), pp. 11578\u201311584. IEEE International Conference on Robotics and Automation ICRA, IEEE; IEEE Robot. Automat. Soc. (2024). https:\/\/doi.org\/10.1109\/ICRA57147.2024.10611329, iEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, 13\u201317 May 2024","DOI":"10.1109\/ICRA57147.2024.10611329"},{"key":"25_CR11","doi-asserted-by":"publisher","unstructured":"Li, W., Que, D., Omisore, O.M.: A gaussian-based guidewire segmentation and tracking method for teleoperated robotic intravascular interventions. In: 2021 5TH International Conference on Robotics and Automation Sciences (ICRAS 2021), pp. 38\u201343. IEEE (2021). https:\/\/doi.org\/10.1109\/ICRAS52289.2021.9476273, 5th International Conference on Robotics and Automation Sciences (ICRAS), China Univ Geosciences, Wuhan, Peoples R China, 11\u201313 June 2021","DOI":"10.1109\/ICRAS52289.2021.9476273"},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"Mei, Z., et al.: Transferring virtual surgical skills to reality: AI agents mastering surgical decision-making in vascular interventional robotics. IEEE-ASME Trans. Mechatron. (2024). https:\/\/doi.org\/10.1109\/TMECH.2024.3420954","DOI":"10.1109\/TMECH.2024.3420954"},{"key":"25_CR13","doi-asserted-by":"publisher","first-page":"178450","DOI":"10.1109\/access.2020.3027923","volume":"8","author":"MQ Mohammed","year":"2020","unstructured":"Mohammed, M.Q., Chung, K.L., Chyi, C.S.: Review of deep reinforcement learning-based object grasping: techniques, open challenges, and recommendations. IEEE Access 8, 178450\u2013178481 (2020). https:\/\/doi.org\/10.1109\/access.2020.3027923","journal-title":"IEEE Access"},{"issue":"3","key":"25_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.tvir.2023.100918","volume":"26","author":"E Morag","year":"2023","unstructured":"Morag, E., Cornelis, F.H., Weisz, G., Gandhi, R.: Overcoming barriers and advancements in endovascular robotics: a review of systems and developments. Tech. Vasc. Interv. Radiol. 26(3), 100918 (2023)","journal-title":"Tech. Vasc. Interv. Radiol."},{"key":"25_CR15","doi-asserted-by":"publisher","unstructured":"Omisore, O.M., Akinyemi, T.O., Duan, W., Du, W., Wang, L.: Multi-lateral branched network for tool segmentation during robot-assisted endovascular interventions. IEEE Trans. Med. Robot. Bionics 6(2), 433\u2013447 (2024). https:\/\/doi.org\/10.1109\/TMRB.2024.3369765","DOI":"10.1109\/TMRB.2024.3369765"},{"key":"25_CR16","doi-asserted-by":"publisher","unstructured":"Popov, M., et al.: Dataset for automatic region-based coronary artery disease diagnostics using x-ray angiography images. Sci. Data 11(1) (2024). https:\/\/doi.org\/10.1038\/s41597-023-02871-z","DOI":"10.1038\/s41597-023-02871-z"},{"key":"25_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"25_CR18","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A., Zhu, M.L., Zhmoginov, A., Chen, L.C.: IEEE: Mobilenetv2: inverted residuals and linear bottlenecks. In: 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510\u20134520. IEEE Conference on Computer Vision and Pattern Recognition, IEEE, New York (2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00474","DOI":"10.1109\/cvpr.2018.00474"},{"key":"25_CR19","doi-asserted-by":"publisher","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2017). https:\/\/doi.org\/10.1109\/tpami.2016.2572683, shelhamer, Evan Long, Jonathan Darrell, Trevor 1939-3539","DOI":"10.1109\/tpami.2016.2572683"},{"key":"25_CR20","doi-asserted-by":"publisher","unstructured":"Song, H.S., Yi, B.J., Won, J.Y., Woo, J.: Learning-based catheter and guidewire-driven autonomous vascular intervention robotic system for reduced repulsive force. J. Comput. Des. Eng. 9(5), 1549\u20131564 (2022). https:\/\/doi.org\/10.1093\/jcde\/qwac074","DOI":"10.1093\/jcde\/qwac074"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Yan, Y., et al.: A novel sea-based haptic interface for robot-assisted vascular interventional surgery. In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 5871\u20135876. IEEE (2024)","DOI":"10.1109\/ICRA57147.2024.10611034"},{"key":"25_CR22","unstructured":"Yarats, D., Kostrikov, I., Fergus, R.: Image augmentation is all you need: regularizing deep reinforcement learning from pixels. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"25_CR23","doi-asserted-by":"publisher","unstructured":"Zhang, G., Wong, H.C., Zhu, J., An, T., Wang, C.: Jigsaw training-based background reverse attention transformer network for guidewire segmentation. Int. J. Comput. Assist. Radiol. Surg. 18(4), 653\u2013661 (2023). https:\/\/doi.org\/10.1007\/s11548-022-02803-z","DOI":"10.1007\/s11548-022-02803-z"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2025: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7072-0_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:09:14Z","timestamp":1773274154000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7072-0_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819570713","9789819570720"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7072-0_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wellington","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}