0018-9294 (c) 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TBME.2016.2622361, IEEE Transactions on Biomedical Engineering > TBME-01244-2016 < 1 Abstract—Continuum robots provide inherent structural compliance with high dexterity to access the surgical target sites along tortuous anatomical paths under constrained environments, and enable to perform complex and delicate operations through small incisions in minimally invasive surgery. These advantages enable their broad applications with minimal trauma, and make challenging clinical procedures possible with miniaturized instrumentation and high curvilinear access capabilities. However, their inherent deformable designs make it difficult to realize three-dimensional (3D) intraoperative real- time shape sensing to accurately model their shape. Solutions to this limitation can lead themselves to further develop closely associated techniques of closed-loop control, path planning, human–robot interaction and surgical manipulation safety concerns in minimally invasive surgery. Although extensive model-based research that relies on kinematics and mechanics has been performed, accurate shape sensing of continuum robots remains challenging, particularly in cases of unknown and dynamic payloads. This survey investigates the recent advances in alternative emerging techniques for 3D shape sensing in this field, and focuses on the following categories: fiber optic sensors based, electromagnetic tracking based and intraoperative imaging modalities based shape reconstruction methods. The limitations of existing technologies and prospects of new technologies are also discussed. Index Terms—shape sensing, shape reconstruction, fiber Bragg grating, continuum robot, electromagnetic tracking, intraoperative imaging modalities. Manuscript received May 30, 2016. This work is supported by the Singapore Academic Research Fund under Grant R-397-000-227-112, NMRC Bedside & Bench under grant R-397-000-245-511 and Singapore Millennium Foundation under Grant R-397-000-201-592. C. Shi and X. Luo contributed equally to this work. Corresponding authors: Xiongbiao Luo and Hongliang Ren. C. Shi is with Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (e-mail: chaoyanghit@gmail.com). X. Luo is with Department of Computer Science, Xiamen University, Fujian 361005, China, (e-mail: xiongbiao.luo@gmail.com). P. Qi is with Department of Control Science & Engineering, College of Electronics and Information Engineering, Tongji University, Shanghai, China. T. Li and H. Ren are with the Department of Biomedical Engineering, National University of Singapore, Singapore, (e-mail: ren@nus.edu.sg ). S. Song is with School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China. Z. Najdovski is with the Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3216, Australia. T. Fukuda is with Department of Micro-nano Systems Engineering, Nagoya University, Nagoya, 464-8603, JAPAN. I. INTRODUCTION n recent years, continuum robots have offered significant advantages and demonstrated great promise in terms of technological advances and extensive clinical applications in minimally invasive surgery (MIS) [1-4]. MIS procedures involve performing delicate operations on anatomical structures through small incisions or natural orifices along tortuous paths inside the human body, resulting in both access and operational constraints, and furthermore technical challenges [2, 5-7]. Continuum robots provide not only curvilinear and flexible accessibility through these small incisions or orifices, but are also capable of generating large forces at the distal ends to support various operations [1, 4, 8, 9]. They are defined as actuatable structures whose constitutive materials form curves with continuous tangent vectors [1], including concentric tube robots, active cable/tendon-driven catheters and needles, single-backbone and multi-backbone continuum robots, and pneumatically and hydraulically driven continuum manipulators. Their compliant structures with high flexibility and precision allow reaching targeted treatment sites with complex morphologies and tortuous path access under constrained spaces, and enable to complete complex and delicate operations inside the patients’ body through small incisions. Such advantages are beneficial for patients with reduced blood loss, minimal trauma, fewer postoperative complications and shorter recovery time, and improve the current clinical procedures and make new workflow possible [1, 4, 7]. Consequently, they have been increasingly and extensively introduced into various MIS procedures, including otolaryngology, ophthalmic surgery, neurosurgery, abdominal surgery and intravascular interventions, particularly for cardiac surgery and stenting surgery [1, 7, 10-14]. However, to achieve precise and reliable motion control of continuum robots used in these surgical procedures requires accurate and real-time shape sensing. Due to their inherent deformable design and inevitable collisions with the anatomy during surgical procedures, accurately modeling their shape remains a challenge [15, 16]. Intensive model-based shape reconstruction methods that rely on kinematics and mechanics, have been developed for closed-loop control, path planning and collision detection [1, 16-25]. Their fundamental approach typically endeavors to balance the tradeoffs among mathematical model complexity, accuracy, and computational expense [4, 15, 24, 26, 27]. The accuracy of these modeling Shape Sensing Techniques for Continuum Robots in Minimally Invasive Surgery: A Survey Chaoyang Shi, Xiongbiao Luo, Peng Qi, Tianliang Li, Shuang Song, Zoran Najdovski, Toshio Fukuda, Fellow, IEEE, Hongliang Ren I