@article{Roberti2020,
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}
}

@article{Leporini2020,
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}
}

@article{Sayols2019,
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}
}

@article{Oleari2019,
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}
}