{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T04:16:25Z","timestamp":1775708185567,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100020963","name":"Moonshot Research and Development Program","doi-asserted-by":"publisher","award":["JPMJMS2214-02"],"award-info":[{"award-number":["JPMJMS2214-02"]}],"id":[{"id":"10.13039\/501100020963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004721","name":"The University of Tokyo","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004721","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"Abstract<\/jats:title>\n \n Purpose<\/jats:title>\n Countertraction is a vital technique in laparoscopic surgery, stretching the tissue surface for incision and dissection. Due to the technical challenges and frequency of countertraction, autonomous countertraction has the potential to significantly reduce surgeons\u2019 workload. Despite several methods proposed for automation, achieving optimal tissue visibility and tension for incision remains unrealized. Therefore, we propose a method for autonomous countertraction that enhances tissue surface planarity and visibility.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We constructed a neural network that integrates a point cloud convolutional neural network (CNN) with a deep reinforcement learning (RL) model. This network continuously controls the forceps position based on the surface shape observed by a camera and the forceps position. RL is conducted in a physical simulation environment, with verification experiments performed in both simulation and phantom environments. The evaluation was performed based on plane error, representing the average distance between the tissue surface and its least-squares plane, and angle error, indicating the angle between the tissue surface vector and the camera\u2019s optical axis vector.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The plane error decreased under all conditions both simulation and phantom environments, with 93.3% of case showing a reduction in angle error. In simulations, the plane error decreased from \n \n $$3.6 \\pm 1.5{\\text{ mm}}$$<\/jats:tex-math>\n \n \n 3.6<\/mml:mn>\n \u00b1<\/mml:mo>\n 1.5<\/mml:mn>\n \n \n mm<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula> to \n \n $$1.1 \\pm 1.8 {\\text{mm}}$$<\/jats:tex-math>\n \n \n 1.1<\/mml:mn>\n \u00b1<\/mml:mo>\n 1.8<\/mml:mn>\n mm<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>, and the angle error from \n \n $$29 \\pm 19 ^\\circ$$<\/jats:tex-math>\n \n \n 29<\/mml:mn>\n \u00b1<\/mml:mo>\n \n 19<\/mml:mn>\n \u2218<\/mml:mo>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula> to \n \n $$14 \\pm 13 ^\\circ$$<\/jats:tex-math>\n \n \n 14<\/mml:mn>\n \u00b1<\/mml:mo>\n \n 13<\/mml:mn>\n \u2218<\/mml:mo>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>. In the phantom environment, the plane error decreased from \n \n $$0.96 \\pm 0.24{\\text{ mm}}$$<\/jats:tex-math>\n \n \n 0.96<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.24<\/mml:mn>\n \n \n mm<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula> to \n \n $$0.39 \\pm 0.23 {\\text{mm}}$$<\/jats:tex-math>\n \n \n 0.39<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.23<\/mml:mn>\n mm<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>, and the angle error from \n \n $$32 \\pm 29 ^\\circ$$<\/jats:tex-math>\n \n \n 32<\/mml:mn>\n \u00b1<\/mml:mo>\n \n 29<\/mml:mn>\n \u2218<\/mml:mo>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula> to \n \n $$17 \\pm 20 ^\\circ$$<\/jats:tex-math>\n \n \n 17<\/mml:mn>\n \u00b1<\/mml:mo>\n \n 20<\/mml:mn>\n \u2218<\/mml:mo>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The proposed neural network was validated in both simulation and phantom experimental settings, confirming that traction control improved tissue planarity and visibility. These results demonstrate the feasibility of automating countertraction using the proposed model.<\/jats:p>\n <\/jats:sec>","DOI":"10.1007\/s11548-024-03264-2","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T18:06:41Z","timestamp":1726510001000},"page":"625-633","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Autonomous countertraction for secure field of view in laparoscopic surgery using deep reinforcement learning"],"prefix":"10.1007","volume":"20","author":[{"given":"Yuriko","family":"Iyama","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudai","family":"Takahashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahe","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takumi","family":"Noda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuaki","family":"Hara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Etsuko","family":"Kobayashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ichiro","family":"Sakuma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5485-4883","authenticated-orcid":false,"given":"Naoki","family":"Tomii","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,16]]},"reference":[{"key":"3264_CR1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.bpobgyn.2005.10.003","volume":"20","author":"R Garry","year":"2006","unstructured":"Garry R (2006) Laparoscopic surgery. Best Pract Res Clin Obstet Gynaecol 20:89\u2013104","journal-title":"Best Pract Res Clin Obstet Gynaecol"},{"key":"3264_CR2","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1007\/s11701-021-01358-6","volume":"16","author":"J Morton","year":"2022","unstructured":"Morton J, Stewart GD (2022) The burden of performing minimal access surgery: ergonomics survey results from 462 surgeons across Germany, the UK and the USA. J Robot Surg 16:1347\u20131354","journal-title":"J Robot Surg"},{"key":"3264_CR3","doi-asserted-by":"publisher","first-page":"e19","DOI":"10.1016\/j.jss.2011.08.004","volume":"172","author":"GPY Szeto","year":"2012","unstructured":"Szeto GPY, Cheng SWK, Poon JTC, Ting ACW, Tsang RCC, Ho P (2012) Surgeons\u2019 Static Posture and Movement Repetitions in Open and Laparoscopic Surgery. J Surg Res 172:e19\u2013e31","journal-title":"J Surg Res"},{"key":"3264_CR4","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1126\/scitranslmed.aad9398","volume":"8","author":"A Shademan","year":"2016","unstructured":"Shademan A, Decker RS, Opfermann JD, Leonard S, Kriger A, Kim PCW (2016) Supervised autonomous robotic soft tissue surgery. Sci Transl Med 8:337","journal-title":"Sci Transl Med"},{"key":"3264_CR5","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1111\/ases.12283","volume":"9","author":"A Kondo","year":"2016","unstructured":"Kondo A, Nishizawa Y, Ito M, Saito N, Fuji S (2016) Relationship between tissue tension and thermal diffusion to peripheral tissue using an energy device. Asian J Endosc Surg 9:226\u2013230","journal-title":"Asian J Endosc Surg"},{"key":"3264_CR6","doi-asserted-by":"publisher","first-page":"76","DOI":"10.5963\/LSMR0302005","volume":"3","author":"Y Kurita","year":"2013","unstructured":"Kurita Y, Tsuji T, Kawahara T (2013) Force-based automatic classification of basic manipulations with grasping forceps. Int J Life Sci Med Res 3:76\u201382","journal-title":"Int J Life Sci Med Res"},{"key":"3264_CR7","first-page":"5259","volume":"2013","author":"D Navarro-Alarcon","year":"2013","unstructured":"Navarro-Alarcon D, Liu Y, Romero JG, Li P (2013) Visually servoed deformation control by robot manipulators. IEEE Int Conf Robotics Automat 2013:5259\u20135264","journal-title":"IEEE Int Conf Robotics Automat"},{"key":"3264_CR8","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1177\/0278364914529355","volume":"33","author":"D Navarro-Alarcon","year":"2014","unstructured":"Navarro-Alarcon D, Liu Y, Romero JG, Li P (2014) On the visual deformation servoing of compliant objects: uncalibrated control methods and experiments. Int J Robot Res 33:1462\u20131480","journal-title":"Int J Robot Res"},{"key":"3264_CR9","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1109\/TRO.2016.2533639","volume":"32","author":"D Navarro-Alarcon","year":"2016","unstructured":"Navarro-Alarcon D, Yip HM, Wang Z, Liu Y-H, Zhong F, Zhang T, Li P (2016) Automatic 3-D manipulation of soft objects by robotic arms with an adaptive deformation model. IEEE Trans Robot 32:429\u2013441","journal-title":"IEEE Trans Robot"},{"key":"3264_CR10","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1109\/TRO.2017.2765333","volume":"34","author":"D Navarro-Alarcon","year":"2018","unstructured":"Navarro-Alarcon D, Liu Y-H (2018) Fourier-based shape servoing: a new feedback method to actively deform soft objects into desired 2-D image contours. IEEE Trans Robot 34:272\u2013279","journal-title":"IEEE Trans Robot"},{"key":"3264_CR11","doi-asserted-by":"publisher","first-page":"2985","DOI":"10.1109\/TMECH.2021.3126383","volume":"27","author":"J Qi","year":"2022","unstructured":"Qi J, Ma G, Zhu J, Zhou P, Lyu Y, Zhang H, Navarro-Alarcon D (2022) Contour moments based manipulation of composite rigid-deformable objects with finite time model estimation and shape\/position control. IEEEASME Trans Mechatron 27:2985\u20132996","journal-title":"IEEEASME Trans Mechatron"},{"key":"3264_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2021.103798","volume":"142","author":"J Zhu","year":"2021","unstructured":"Zhu J, Navarro-Alarcon D, Passama R, Cherubini A (2021) Vision-based manipulation of deformable and rigid objects using subspace projections of 2D contours. Robot Auton Syst 142:103798","journal-title":"Robot Auton Syst"},{"key":"3264_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101964","volume":"109","author":"A Coronato","year":"2020","unstructured":"Coronato A, Naeem M, Pietro GD, Paragliola G (2020) Reinforcement learning for intelligent healthcare applications: a survey. Artif Intell Med 109:101964","journal-title":"Artif Intell Med"},{"key":"3264_CR14","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s10846-017-0468-y","volume":"86","author":"AS Polydoros","year":"2017","unstructured":"Polydoros AS, Nalpantidis L (2017) Survey of model-based reinforcement learning: applications on robotics. J Intell Robot Syst 86:153\u2013173","journal-title":"J Intell Robot Syst"},{"key":"3264_CR15","first-page":"2371","volume":"2017","author":"B Thananjeyan","year":"2017","unstructured":"Thananjeyan B, Garg A, Krishnan S, Chen C, Miller L, Goldberg K (2017) Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning. IEEE Int Conf Robotics Autom (ICRA) 2017:2371\u20132378","journal-title":"IEEE Int Conf Robotics Autom (ICRA)"},{"key":"3264_CR16","first-page":"1","volume":"2019","author":"ND Nguyen","year":"2019","unstructured":"Nguyen ND, Nguyen T, Nahavandi S, Bhatti A, Guest G (2019) Manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery. IEEE Int Sys Conf (SysCon) 2019:1\u20137","journal-title":"IEEE Int Sys Conf (SysCon)"},{"key":"3264_CR17","first-page":"1339","volume":"2019","author":"T Nguyen","year":"2019","unstructured":"Nguyen T, Nguyen ND, Bello F, Nahavandi S (2019) A New tensioning method using deep reinforcement learning for surgical pattern cutting. IEEE Int Conf on Indust Technol (ICIT) 2019:1339\u20131344","journal-title":"IEEE Int Conf on Indust Technol (ICIT)"},{"key":"3264_CR18","unstructured":"Haarnoja T, Zhou A, Abbeel P, Levine S, Dy J, Krause A (2018) Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, In: Dy J, Krause A (Eds.), Proceedings of the 35th International Conference on Machine Learning, PMLR, 1861\u20131870."},{"key":"3264_CR19","doi-asserted-by":"crossref","unstructured":"Wu W, Qi Z, Fuxin L (2019) PointConv Deep Convolutional Networks on 3D Point Clouds, Proceedings of the IEEECVF Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2019.00985"},{"key":"3264_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2601097.2601152","volume":"33","author":"M Macklin","year":"2014","unstructured":"Macklin M, M\u00fcller M, Chentanez N, Kim T-Y (2014) Unified particle physics for real-time applications. ACM Trans Graph 33:1\u201312","journal-title":"ACM Trans Graph"},{"key":"3264_CR21","unstructured":"Lin X, Wang Y, Olkin J, Held D, Kober J, Ramos F, Tomlin C (2021) SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation, In: Kober J, Ramos F, Tomlin C (Eds.), Proceedings of the 2020 Conference on Robot Learning, PMLR, 432\u2013448."},{"key":"3264_CR22","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.jvcir.2007.01.005","volume":"18","author":"M M\u00fcller","year":"2007","unstructured":"M\u00fcller M, Heidelberger B, Hennix M, Ratcliff J (2007) Position based dynamics. J Vis Commun Image Represent 18:109\u2013118","journal-title":"J Vis Commun Image Represent"},{"key":"3264_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3748\/wjg.v28.i1.1","volume":"28","author":"M Nagata","year":"2022","unstructured":"Nagata M (2022) Advances in traction methods for endoscopic submucosal dissection: What is the best traction method and traction direction? World J Gastroenterol 28:1","journal-title":"World J Gastroenterol"},{"key":"3264_CR24","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.cmpb.2018.02.006","volume":"158","author":"L Chen","year":"2018","unstructured":"Chen L, Tang W, John NW, Wan TR, Zhang JJ (2018) SLAM-based dense surface reconstruction in monocular Minimally Invasive Surgery and its application to Augmented Reality. Comput Method Program Biomed 158:135\u2013146","journal-title":"Comput Method Program Biomed"},{"key":"3264_CR25","doi-asserted-by":"crossref","unstructured":"Stoyanov D, Ayache N, Delingette H, Golland P, Mori K (2012) Stereoscopic Scene Flow for Robotic Assisted Minimally Invasive Surgery. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2012, Berlin, Heidelberg, Springer, 479\u2013486.","DOI":"10.1007\/978-3-642-33415-3_59"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03264-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-024-03264-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03264-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,27]],"date-time":"2025-04-27T10:02:35Z","timestamp":1745748155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-024-03264-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,16]]},"references-count":25,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["3264"],"URL":"https:\/\/doi.org\/10.1007\/s11548-024-03264-2","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,16]]},"assertion":[{"value":"31 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This research was supported by the JST Moonshot Research and Development Project (grant number JPMJMS2214-02).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}