Andrea Roberti Nicola Piccinelli, Daniele Meli Riccardo Muradore ; Paolo Fiorini, Improving Rigid 3-D Calibration for Robotic Surgery Journal Article IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, 2 (4), pp. 569-573, 2020, ISBN: 2576-3202. Abstract | Links | BibTeX | Tags: Calibration, Medical robotics, Minimally invasive surgery, multi arm calibration, Robot, Robot vision systems, Surgery, Surgical robotics, Three-dimensional displays Narcís Sayols Alessio Sozzi, Nicola Piccinelli Albert Hernansanz Alicia Casals Marcello Bonfè ; Riccardo Muradore, 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020. Abstract | Links | BibTeX | Tags: assistive tasks, autonomous execution, autonomous surgical, Collision avoidance, collision free connections, collision-free trajectories, desired task, developed motion planner, dynamical systems based obstacle avoidance, final target, geometric constraints, global level computes smooth spline-based trajectories, Medical robotics, mobile robots, motion control, moving obstacles, realistic surgical scenario, Robots, splines (mathematics), Surgery, surgery INSPEC: Non-Controlled Indexing robotic minimally invasive surgery, Task analysis, Tools, Trajectory, two-layer architecture V. Singh Bawa G. Singh, Kaping’A Skarga-Bandurova Leporini Landolfo Stabile Setti Muradore Oleari Cuzzolin F I A C A F R E F ESAD: Endoscopic Surgeon Action Detection Dataset Online arXiv, (Ed.): 2020, visited: 25.06.2020. Abstract | Links | BibTeX | Tags: Action detection, endoscopic video, surgeon action detection, Surgery Alice Leporini Elettra Oleari, Carmela Landolfo Alberto Sanna Alessandro Larcher Giorgio Gandaglia Nicola Fossati Fabio Muttin Umberto Capitanio Francesco Montorsi Andrea Salonia Marco Minelli Federica Ferraguti Cristian Secchi Saverio Farsoni Alessio Sozzi Marcello Bonf`e Narcis Sayols Albert Hernansanz Alicia Casals Sabine Hertle Fabio Cuzzolin Andrew Dennison Andreas Melzer Gernot Kronreif Salvatore Siracusano Fabio Falezza Francesco Setti ; Muradore, Riccardo Technical and Functional Validation of a Teleoperated Multirobots Platform for Minimally Invasive Surgery Journal Article IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, 2 (2), pp. 148-156, 2020, ISSN: 2576-3202. Abstract | Links | BibTeX | Tags: functional evaluation, Instruments, Manipulators, Protocols, Robot kinematics, robotic end effector task metrics, Surgery, surgical-related tasks, tele-operated surgical robotic system, Tools, Validation protocol Giacomo De Rossi Marco Minelli, Alessio Sozzi Nicola Piccinelli Federica Ferraguti Francesco Setti Marcello Bonfé Christian Secchi Riccardo Muradore 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2020, ISBN: 978-1-7281-4004-9. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Collision avoidance, Manipulators, Medical robotics, mobile robots, predictive control, Robot, Robot vision, Surgery, trajectory control, uncertain systems Casals, Alicia; Hernansanz, Albert; Sayols, Narcís; Amat, Josep Assistance Strategies for Robotized Laparoscopy Conference Robot 2019: Fourth Iberian Robotics Conference, 2019, ISBN: 978-3-030-36149-5. Abstract | Links | BibTeX | Tags: Cooperative robotics, Laparoscopy, Robot, Safety, Surgery, Surgical robots, Virtual feedback Sayols, Narcís; Hernansanz, Albert; Parra, Johanna; Eixarch, Elisenda; Gratacós, Eduard; Amat, Josep; Casals, Alícia Vision Based Robot Assistance in TTTS Fetal Surgery Journal Article 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019. Abstract | Links | BibTeX | Tags: Coagulation, Image processing, Laser, Robot, Surgery, Three-dimensional displays, Visualization Oleari, Elettra; Leporini, Alice; Trojaniello, Diana; Sanna, Alberto; Capitanio, Umberto; Deho, Federico; Larcher, Alessandro; Montorsi, Francesco; Salonia, Andrea; Setti, Francesco; Muradore, Riccardo Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures Journal Article pp. 1-6, 2019, ISBN: 978-1-7281-2342-4. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Autonomy, Cognitive control, Cognitive functions, Decision making, Laparoscopes, Laparoscopy, Laparoscopy, learning systems, machine learning, Medical robotics, multirobots teleoperated platform, Robotic surgery, Surgery, Surgical robots, Teleoperation Setti, Francesco; Oleari, Elettra; Leporini, Alice; Trojaniello, Diana; Sanna, Alberto; Capitanio, Umberto; Montorsi, Francesco; Salonia, Andrea; Muradore, Riccardo 2019, ISBN: 978-1-5386-7825-1. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Cognitive control, Computer Science, Laparoscopy, Laparoscopy, machine learning, Robot, Robotic surgery, Surgery, Teleoperation Hernansanz, Albert; Pieras, ; Ferrandiz, ; Moreno, ; Casals, Alicia Sentisim: a hybrid training platform for sinb in local melanoma staging Conference CRAS 2019, 2019. Abstract | Links | BibTeX | Tags: Anatomical trainer, Biopsy, Melanoma, Simulator, Surgery, Surgical trainer Hernansanz, Albert; Martínez, ; Rovira, ; Casals, Alicia A physical/virtual platform for hysteroscopy training Conference Proceedings of the 9th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery, 2019. Abstract | Links | BibTeX | Tags: Computer Science, Endoscopy, Laparoscopy, Laparoscopy, Robot, Robotic Surgery, Robotic surgery, Surgery, Surgical robots, Training Arturo, Marbán; Srinivasan, Vignesh; Samek, Wojciech; Fernández, Josep; Casals, Alicia 2018. Abstract | Links | BibTeX | Tags: Learning, Robot, Robotic surgery, Robotics, Surgery, Training
2020
title = {Improving Rigid 3-D Calibration for Robotic Surgery},
author = {Andrea Roberti , Nicola Piccinelli , Daniele Meli, Riccardo Muradore , and Paolo Fiorini ,},
editor = {IEEE },
doi = {10.1109/TMRB.2020.3033670},
isbn = {2576-3202},
year = {2020},
date = {2020-11-04},
journal = {IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS},
volume = {2},
number = {4},
pages = {569-573},
abstract = {Autonomy is the next frontier of research in robotic surgery and its aim is to improve the quality of surgical procedures in the next future. One fundamental requirement for autonomy is advanced perception capability through vision sensors. In this article, we propose a novel calibration technique for a surgical scenario with a da Vinci ® Research Kit (dVRK) robot. Camera and robotic arms calibration are necessary to precise position and emulate expert surgeon. The novel calibration technique is tailored for RGB-D cameras. Different tests performed on relevant use cases prove that we significantly improve precision and accuracy with respect to state of the art solutions for similar devices on a surgical-size setups. Moreover, our calibration method can be easily extended to standard surgical endoscope used in real surgical scenario.},
keywords = {Calibration, Medical robotics, Minimally invasive surgery, multi arm calibration, Robot, Robot vision systems, Surgery, Surgical robotics, Three-dimensional displays},
pubstate = {published},
tppubtype = {article}
}
title = {Global/local motion planning based on Dynamic Trajectory Reconfiguration and Dynamical Systems for autonomous surgical robots},
author = {Narcís Sayols, Alessio Sozzi, Nicola Piccinelli, Albert Hernansanz, Alicia Casals, Marcello Bonfè, and Riccardo Muradore,},
editor = {IEEE},
doi = {10.1109/ICRA40945.2020.9197525},
year = {2020},
date = {2020-09-15},
booktitle = {2020 IEEE International Conference on Robotics and Automation (ICRA)},
abstract = {This paper addresses the generation of collision-free trajectories for the autonomous execution of assistive tasks in Robotic Minimally Invasive Surgery (R-MIS). The proposed approach takes into account geometric constraints related to the desired task, like for example the direction to approach the final target and the presence of moving obstacles. The developed motion planner is structured as a two-layer architecture: a global level computes smooth spline-based trajectories that are continuously updated using virtual potential fields; a local level, exploiting Dynamical Systems based obstacle avoidance, ensures collision free connections among the spline control points. The proposed architecture is validated in a realistic surgical scenario.},
keywords = {assistive tasks, autonomous execution, autonomous surgical, Collision avoidance, collision free connections, collision-free trajectories, desired task, developed motion planner, dynamical systems based obstacle avoidance, final target, geometric constraints, global level computes smooth spline-based trajectories, Medical robotics, mobile robots, motion control, moving obstacles, realistic surgical scenario, Robots, splines (mathematics), Surgery, surgery INSPEC: Non-Controlled Indexing robotic minimally invasive surgery, Task analysis, Tools, Trajectory, two-layer architecture},
pubstate = {published},
tppubtype = {conference}
}
title = {ESAD: Endoscopic Surgeon Action Detection Dataset},
author = {V. Singh Bawa, G. Singh, F. Kaping’A, I. Skarga-Bandurova, A. Leporini, C. Landolfo, A. Stabile, F. Setti, R. Muradore, E. Oleari, F. Cuzzolin},
editor = {arXiv},
url = {https://zenodo.org/record/4471476#.YBFMT-hKiXI},
year = {2020},
date = {2020-06-12},
urldate = {2020-06-25},
abstract = {In this work, we take aim towards increasing the effectiveness of surgical assistant robots. We intended to make assistant robots safer by making them aware about the actions of surgeon, so it can take appropriate assisting actions. In other words, we aim to solve the problem of surgeon action detection in endoscopic videos. To this, we introduce a challenging dataset for surgeon action detection in real-world endoscopic videos. Action classes are picked based on the feedback of surgeons and annotated by medical professional. Given a video frame, we draw bounding box around surgical tool which is performing action and label it with action label. Finally, we presenta frame-level action detection baseline model based on recent advances in ob-ject detection. Results on our new dataset show that our presented dataset provides enough interesting challenges for future method and it can serveas strong benchmark corresponding research in surgeon action detection in endoscopic videos.},
keywords = {Action detection, endoscopic video, surgeon action detection, Surgery},
pubstate = {published},
tppubtype = {online}
}
title = {Technical and Functional Validation of a Teleoperated Multirobots Platform for Minimally Invasive Surgery},
author = {Alice Leporini, Elettra Oleari, Carmela Landolfo, Alberto Sanna, Alessandro Larcher, Giorgio Gandaglia, Nicola Fossati, Fabio Muttin, Umberto Capitanio, Francesco Montorsi, Andrea Salonia, Marco Minelli, Federica Ferraguti, Cristian Secchi, Saverio Farsoni, Alessio Sozzi, Marcello Bonf`e, Narcis Sayols, Albert Hernansanz, Alicia Casals, Sabine Hertle, Fabio Cuzzolin, Andrew Dennison, Andreas Melzer, Gernot Kronreif, Salvatore Siracusano, Fabio Falezza, Francesco Setti and Riccardo Muradore},
editor = {IEEE},
doi = {10.1109/TMRB.2020.2990286},
issn = {2576-3202},
year = {2020},
date = {2020-05-18},
journal = {IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS},
volume = {2},
number = {2},
pages = {148-156},
abstract = {Nowadays Robotic assisted Minimally Invasive Surgeries (R-MIS) are the elective procedures for treating highly accurate and scarcely invasive pathologies, thanks to their ability to empower surgeons’ dexterity and skills. The research on new Multi-Robots Surgery (MRS) platform is cardinal to the development of a new SARAS surgical robotic platform, which aims at carrying out autonomously the assistants tasks during R-MIS procedures. In this work, we will present the SARAS MRS platform validation protocol, framed in order to assess: (i) its technical performances in purely dexterity exercises, and (ii) its functional performances. The results obtained show a prototype able to put the users in the condition of accomplishing the tasks requested (both dexterity- and surgical-related), even with reasonably lower performances respect to the industrial standard. The main aspects on which further improvements are needed result to be the stability of the end effectors, the depth perception and the vision systems, to be enriched with dedicated virtual fixtures. The SARAS’ aim is to reduce the main surgeon’s workload through the automation of assistive tasks which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time.},
keywords = {functional evaluation, Instruments, Manipulators, Protocols, Robot kinematics, robotic end effector task metrics, Surgery, surgical-related tasks, tele-operated surgical robotic system, Tools, Validation protocol},
pubstate = {published},
tppubtype = {article}
}
title = {Cognitive Robotic Architecture for Semi-Autonomous Execution of Manipulation Tasks in a Surgical Environment},
author = {Giacomo De Rossi, Marco Minelli, Alessio Sozzi, Nicola Piccinelli, Federica Ferraguti, Francesco Setti, Marcello Bonfé, Christian Secchi, Riccardo Muradore},
editor = {IEEE International Intelligent Robots and Systems (IROS)},
doi = {10.1109/IROS40897.2019.8967667},
isbn = {978-1-7281-4004-9},
year = {2020},
date = {2020-01-27},
booktitle = {2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
publisher = {IEEE},
abstract = {The development of robotic systems with a certain level of autonomy to be used in critical scenarios, such as an operating room, necessarily requires a seamless integration of multiple state-of-the-art technologies. In this paper we propose a cognitive robotic architecture that is able to help an operator accomplish a specific task. The architecture integrates an action recognition module to understand the scene, a supervisory control to make decisions, and a model predictive control to plan collision-free trajectory for the robotic arm taking into account obstacles and model uncertainty. The proposed approach has been validated on a simplified scenario involving only a da VinciO surgical robot and a novel manipulator holding standard laparoscopic tools.},
keywords = {Artificial Intelligence, Collision avoidance, Manipulators, Medical robotics, mobile robots, predictive control, Robot, Robot vision, Surgery, trajectory control, uncertain systems},
pubstate = {published},
tppubtype = {conference}
}
2019
title = {Assistance Strategies for Robotized Laparoscopy},
author = {Alicia Casals and Albert Hernansanz and Narcís Sayols and Josep Amat},
editor = {Springer
},
url = {https://link.springer.com/chapter/10.1007%2F978-3-030-36150-1_40},
doi = {10.1007/978-3-030-36150-1_40},
isbn = {978-3-030-36149-5},
year = {2019},
date = {2019-11-20},
booktitle = {Robot 2019: Fourth Iberian Robotics Conference},
pages = {485-496},
abstract = {Robotizing laparoscopic surgery not only allows achieving better accuracy to operate when a scale factor is applied between master and slave or thanks to the use of tools with 3 DoF, which cannot be used in conventional manual surgery, but also due to additional informatic support. Relying on computer assistance different strategies that facilitate the task of the surgeon can be incorporated, either in the form of autonomous navigation or cooperative guidance, providing sensory or visual feedback, or introducing certain limitations of movements. This paper describes different ways of assistance aimed at improving the work capacity of the surgeon and achieving more safety for the patient, and the results obtained with the prototype developed at UPC.},
keywords = {Cooperative robotics, Laparoscopy, Robot, Safety, Surgery, Surgical robots, Virtual feedback},
pubstate = {published},
tppubtype = {conference}
}
title = {Vision Based Robot Assistance in TTTS Fetal Surgery},
author = {Narcís Sayols and Albert Hernansanz and Johanna Parra and Elisenda Eixarch and Eduard Gratacós and Josep Amat and Alícia Casals},
editor = {IEEE
},
doi = {10.1109/EMBC.2019.8856402},
year = {2019},
date = {2019-10-07},
journal = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
abstract = {This paper presents an accurate and robust tracking vision algorithm for Fetoscopic Laser Photo-coagulation (FLP) surgery for Twin-Twin Transfusion Syndrome (TTTS). The aim of the proposed method is to assist surgeons during anastomosis localization, coagulation and review using a tele-operated robotic system. The algorithm computes the relative position of the fetoscope tool tip with respect to the placenta, via local vascular structure registration. The algorithm uses image features (local superficial vascular structures of the placenta's surface) to automatically match consecutive fetoscopic images. It is composed of three sequential steps: image processing (filtering, binarization and vascular structures segmentation); relevant Points Of Interest (POIs) seletion; and image registration between consecutive images. The algorithm has to deal with the low quality of fetoscopic images, the liquid and dirty environment inside the placenta jointly with the thin diameter of the fetoscope optics and low amount of environment light reduces the image quality. The obtained images are blurred, noisy and with very poor color components. The tracking system has been tested using real video sequences of FLP surgery for TTTS. The computational performance enables real time tracking, locally guiding the robot over the placenta's surface with enough accuracy.},
keywords = {Coagulation, Image processing, Laser, Robot, Surgery, Three-dimensional displays, Visualization},
pubstate = {published},
tppubtype = {article}
}
title = {Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures},
author = {Elettra Oleari and Alice Leporini and Diana Trojaniello and Alberto Sanna and Umberto Capitanio and Federico Deho and Alessandro Larcher and Francesco Montorsi and Andrea Salonia and Francesco Setti and Riccardo Muradore},
editor = {IEEE},
doi = {10.1109/ISMICT.2019.8743931},
isbn = {978-1-7281-2342-4},
year = {2019},
date = {2019-05-09},
pages = {1-6},
abstract = {Nowadays Minimally Invasive Surgery (MIS) is playing an increasingly major role in the clinical practice also thanks to a rapid evolution of the available medical technologies, especially surgical robotics. A new challenge in this respect is to equip robots with cognitive capabilities, in order to make them able to act autonomously and cooperate with human surgeons. In this paper we describe the methodological approach developed to comprehensively describe a specific surgical knowledge, to be transferred to a complex Artificial Intelligence (AI) integrating Perception, Cognitive and Planning modules. Starting from desk researches and a strict cooperation with expert surgeons, the surgical process is framed on a high-level perspective, which is then deepened into a granular model through a Surgical Process Modelling approach, so as to embed all of the needed information by the AI to properly work. The model is eventually completed adding the corresponding Process Risk Analysis. We present the results obtained with the application of the aforementioned methodology to a Laparoscopic Radical Nephrectomy (LRN) procedure and discuss on the next technical implementation of this model.},
keywords = {Artificial Intelligence, Autonomy, Cognitive control, Cognitive functions, Decision making, Laparoscopes, Laparoscopy, Laparoscopy, learning systems, machine learning, Medical robotics, multirobots teleoperated platform, Robotic surgery, Surgery, Surgical robots, Teleoperation},
pubstate = {published},
tppubtype = {article}
}
title = {A Multirobots Teleoperated Platform for Artificial Intelligence Training Data Collection in Minimally Invasive Surgery},
author = {Francesco Setti and Elettra Oleari and Alice Leporini and Diana Trojaniello and Alberto Sanna and Umberto Capitanio and Francesco Montorsi and Andrea Salonia and Riccardo Muradore},
editor = {IEEE},
url = {http://bmvc2018.org/contents/papers/0593.pdf},
doi = {10.1109/ISMR.2019.8710209},
isbn = {978-1-5386-7825-1},
year = {2019},
date = {2019-05-09},
pages = {1-7},
abstract = {Dexterity and perception capabilities of surgical robots may soon be improved by cognitive functions that can support surgeons in decision making and performance monitoring, and enhance the impact of automation within the operating rooms. Nowadays, the basic elements of autonomy in robotic surgery are still not well understood and their mutual interaction is unexplored. Current classification of autonomy encompasses six basic levels: Level 0: no autonomy; Level 1: robot assistance; Level 2: task autonomy; Level 3: conditional autonomy; Level 4: high autonomy. Level 5: full autonomy. The practical meaning of each level and the necessary technologies to move from one level to the next are the subject of intense debate and development. In this paper, we discuss the first outcomes of the European funded project Smart Autonomous Robotic Assistant Surgeon (SARAS). SARAS will develop a cognitive architecture able to make decisions based on pre-operative knowledge and on scene understanding via advanced machine learning algorithms. To reach this ambitious goal that allows us to reach Level 1 and 2, it is of paramount importance to collect reliable data to train the algorithms. We will present the experimental setup to collect the data for a complex surgical procedure (Robotic Assisted Radical Prostatectomy) on very sophisticated manikins (i.e. phantoms of the inflated human abdomen). The SARAS platform allows the main surgeon and the assistant to teleoperate two independent two-arm robots. The data acquired with this platform (videos, kinematics, audio) will be used in our project and will be released (with annotations) for research purposes.},
keywords = {Artificial Intelligence, Cognitive control, Computer Science, Laparoscopy, Laparoscopy, machine learning, Robot, Robotic surgery, Surgery, Teleoperation},
pubstate = {published},
tppubtype = {conference}
}
title = {Sentisim: a hybrid training platform for sinb in local melanoma staging},
author = {Albert Hernansanz and Pieras and Ferrandiz and Moreno and Alicia Casals
},
doi = {10.5281/zenodo.3373320},
year = {2019},
date = {2019-03-21},
publisher = {CRAS 2019},
abstract = {This work presents a new training platform for SLNB in local melanoma staging. This new system solves the previous problems maintaining a realistic scenario, at the same time that measures a series of important parameters to determine the quality of the surgery.},
keywords = {Anatomical trainer, Biopsy, Melanoma, Simulator, Surgery, Surgical trainer},
pubstate = {published},
tppubtype = {conference}
}
title = {A physical/virtual platform for hysteroscopy training},
author = {Albert Hernansanz and Martínez and Rovira and Alicia Casals},
editor = {CRAS 2019},
doi = {10.5281/zenodo.3373297},
year = {2019},
date = {2019-03-21},
booktitle = {Proceedings of the 9th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery},
abstract = {This work presents HysTrainer (HT), a training module for hysteroscopy, which is part of the generic endoscopic training platform EndoTrainer (ET). This platform merges both technologies, with the benefits of having a physical anatomic model and computer assistance for augmented reality and objective assessment. Further to the functions of a surgical trainer, EndoTrainer provides an integral education, training and evaluation platform.},
keywords = {Computer Science, Endoscopy, Laparoscopy, Laparoscopy, Robot, Robotic Surgery, Robotic surgery, Surgery, Surgical robots, Training},
pubstate = {published},
tppubtype = {conference}
}
2018
title = {Estimation of interaction forces in robotic surgery using a semi-supervised deep neural network model},
author = {Marbán Arturo and Vignesh Srinivasan and Wojciech Samek and Josep Fernández and Alicia Casals},
editor = {IEEE},
url = {https://upcommons.upc.edu/bitstream/handle/2117/132610/iros2018_paper_26_07_2018.pdf?sequence=3&isAllowed=y},
doi = {10.1109/IROS.2018.8593701},
year = {2018},
date = {2018-08-09},
abstract = {Providing force feedback as a feature in current Robot-Assisted Minimally Invasive Surgery systems still remains a challenge. In recent years, Vision-Based Force Sensing (VBFS) has emerged as a promising approach to address this problem. Existing methods have been developed in a Supervised Learning (SL) setting. Nonetheless, most of the video sequences related to robotic surgery are not provided with ground-truth force data, which can be easily acquired in a controlled environment. A powerful approach to process unlabeled video sequences and find a compact representation for each video frame relies on using an Unsupervised Learning (UL) method. Afterward, a model trained in an SL setting can take advantage of the available ground-truth force data. In the present work, UL and SL techniques are used to investigate a model in a Semi-Supervised Learning (SSL) framework, consisting of an encoder network and a Long-Short Term Memory (LSTM) network. First, a Convolutional Auto-Encoder (CAE) is trained to learn a compact representation for each RGB frame in a video sequence. To facilitate the reconstruction of high and low frequencies found in images, this CAE is optimized using an adversarial framework and a L1-loss, respectively. Thereafter, the encoder network of the CAE is serially connected with an LSTM network and trained jointly to minimize the difference between ground-truth and estimated force data. Datasets addressing the force estimation task are scarce. Therefore, the experiments have been validated in a custom dataset. The results suggest that the proposed approach is promising.},
keywords = {Learning, Robot, Robotic surgery, Robotics, Surgery, Training},
pubstate = {published},
tppubtype = {conference}
}
Improving Rigid 3-D Calibration for Robotic Surgery Journal Article IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, 2 (4), pp. 569-573, 2020, ISBN: 2576-3202. 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020. ESAD: Endoscopic Surgeon Action Detection Dataset Online arXiv, (Ed.): 2020, visited: 25.06.2020. Technical and Functional Validation of a Teleoperated Multirobots Platform for Minimally Invasive Surgery Journal Article IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, 2 (2), pp. 148-156, 2020, ISSN: 2576-3202. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2020, ISBN: 978-1-7281-4004-9. Assistance Strategies for Robotized Laparoscopy Conference Robot 2019: Fourth Iberian Robotics Conference, 2019, ISBN: 978-3-030-36149-5. Vision Based Robot Assistance in TTTS Fetal Surgery Journal Article 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019. Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures Journal Article pp. 1-6, 2019, ISBN: 978-1-7281-2342-4. 2019, ISBN: 978-1-5386-7825-1. Sentisim: a hybrid training platform for sinb in local melanoma staging Conference CRAS 2019, 2019. A physical/virtual platform for hysteroscopy training Conference Proceedings of the 9th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery, 2019. 2018.
2020
2019
2018