Demir Arikan, Peiyao Zhang, Michael Sommersperger, Shervin Dehghani, Mojtaba Esfandiari, Russel H Taylor, M Ali Nasseri, Peter Gehlbach, Nassir Navab, Iulian Iordachita
{"title":"基于iOCT制导的自主机器人视网膜下注射实时变形感知控制。","authors":"Demir Arikan, Peiyao Zhang, Michael Sommersperger, Shervin Dehghani, Mojtaba Esfandiari, Russel H Taylor, M Ali Nasseri, Peter Gehlbach, Nassir Navab, Iulian Iordachita","doi":"10.1109/icra55743.2025.11128595","DOIUrl":null,"url":null,"abstract":"<p><p>Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space. However, tissue deformations due to tool-tissue interactions constitute a significant challenge in autonomous iOCT-guided robotic subretinal injections. Such interactions impact correct needle positioning and procedure outcomes. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. The technique is achieved through real-time segmentation and 3D reconstruction of the surgical scene from densely sampled iOCT B-scans, which we refer to as B<sup>5</sup>-scans. Using B<sup>5</sup>-scans we monitor the position of the instrument relative to a virtual target layer between the ILM and RPE. Our experiments on <i>ex-vivo</i> porcine eyes demonstrate dynamic adjustment of the insertion depth and overall improved accuracy in needle positioning compared to prior autonomous insertion approaches. Compared to a 35% success rate in subretinal bleb generation with previous approaches, our method reliably created subretinal blebs in 90% our experiments. The source code and data used in this study are publicly available on GitHub.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2025 ","pages":"10531-10537"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422304/pdf/","citationCount":"0","resultStr":"{\"title\":\"Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance.\",\"authors\":\"Demir Arikan, Peiyao Zhang, Michael Sommersperger, Shervin Dehghani, Mojtaba Esfandiari, Russel H Taylor, M Ali Nasseri, Peter Gehlbach, Nassir Navab, Iulian Iordachita\",\"doi\":\"10.1109/icra55743.2025.11128595\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space. However, tissue deformations due to tool-tissue interactions constitute a significant challenge in autonomous iOCT-guided robotic subretinal injections. Such interactions impact correct needle positioning and procedure outcomes. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. The technique is achieved through real-time segmentation and 3D reconstruction of the surgical scene from densely sampled iOCT B-scans, which we refer to as B<sup>5</sup>-scans. Using B<sup>5</sup>-scans we monitor the position of the instrument relative to a virtual target layer between the ILM and RPE. Our experiments on <i>ex-vivo</i> porcine eyes demonstrate dynamic adjustment of the insertion depth and overall improved accuracy in needle positioning compared to prior autonomous insertion approaches. Compared to a 35% success rate in subretinal bleb generation with previous approaches, our method reliably created subretinal blebs in 90% our experiments. The source code and data used in this study are publicly available on GitHub.</p>\",\"PeriodicalId\":73286,\"journal\":{\"name\":\"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. 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Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance.
Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space. However, tissue deformations due to tool-tissue interactions constitute a significant challenge in autonomous iOCT-guided robotic subretinal injections. Such interactions impact correct needle positioning and procedure outcomes. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. The technique is achieved through real-time segmentation and 3D reconstruction of the surgical scene from densely sampled iOCT B-scans, which we refer to as B5-scans. Using B5-scans we monitor the position of the instrument relative to a virtual target layer between the ILM and RPE. Our experiments on ex-vivo porcine eyes demonstrate dynamic adjustment of the insertion depth and overall improved accuracy in needle positioning compared to prior autonomous insertion approaches. Compared to a 35% success rate in subretinal bleb generation with previous approaches, our method reliably created subretinal blebs in 90% our experiments. The source code and data used in this study are publicly available on GitHub.