%0 Journal Article %1 attanasio2020autonomous %A Attanasio, Aleks %A Scaglioni, Bruno %A Leonetti, Matteo %A Frangi, Alejandro F. %A Cross, William %A Biyani, Chandra Shekhar %A Valdastri, Pietro %D 2020 %J IEEE Robotics and Automation Letters %K Medical and for laparoscopy medical robotics,surgical robotics: robots systems,computer vision %N 4 %P 6528--6535 %R 10.1109/LRA.2020.3013914 %T Autonomous Tissue Retraction in Robotic Assisted Minimally Invasive Surgery - A Feasibility Study %V 5 %X In this letter, we describe a novel framework for planning and executing semi-autonomous tissue retraction in minimally invasive robotic surgery. The approach is aimed at removing tissue flaps or connective tissue from the surgical area autonomously, thus exposing the underlying anatomical structures. First, a deep neural network is used to analyse the endoscopic image and detect candidate tissue flaps obstructing the surgical field. A procedural algorithm for planning and executing the retraction gesture is then developed from extended discussions with clinicians. Experimental validation, carried out on a DaVinci Research Kit, shows an average 25\% increase of the visible background after retraction. Another significant contribution of this letter is a dataset containing 1,080 labelled surgical stereo images and the associated depth maps, representing tissue flaps in different scenarios. The work described in this letter is a fundamental step towards the autonomous execution of tissue retraction, and the first example of simultaneous use of deep learning and procedural algorithms. The same framework could be applied to a wide range of autonomous tasks, such as debridement and placement of laparoscopic clips.

@article{attanasio2020autonomous, abstract = {In this letter, we describe a novel framework for planning and executing semi-autonomous tissue retraction in minimally invasive robotic surgery. The approach is aimed at removing tissue flaps or connective tissue from the surgical area autonomously, thus exposing the underlying anatomical structures. First, a deep neural network is used to analyse the endoscopic image and detect candidate tissue flaps obstructing the surgical field. A procedural algorithm for planning and executing the retraction gesture is then developed from extended discussions with clinicians. Experimental validation, carried out on a DaVinci Research Kit, shows an average 25{\%} increase of the visible background after retraction. Another significant contribution of this letter is a dataset containing 1,080 labelled surgical stereo images and the associated depth maps, representing tissue flaps in different scenarios. The work described in this letter is a fundamental step towards the autonomous execution of tissue retraction, and the first example of simultaneous use of deep learning and procedural algorithms. The same framework could be applied to a wide range of autonomous tasks, such as debridement and placement of laparoscopic clips.}, added-at = {2023-05-10T16:23:27.000+0200}, author = {Attanasio, Aleks and Scaglioni, Bruno and Leonetti, Matteo and Frangi, Alejandro F. and Cross, William and Biyani, Chandra Shekhar and Valdastri, Pietro}, biburl = {https://www.bibsonomy.org/bibtex/230a37c7040a878fb31073acb7f441cbb/sassw}, doi = {10.1109/LRA.2020.3013914}, interhash = {7bb3b0facdda9a74d075a5e317844de9}, intrahash = {30a37c7040a878fb31073acb7f441cbb}, issn = {23773766}, journal = {IEEE Robotics and Automation Letters}, keywords = {Medical and for laparoscopy medical robotics,surgical robotics: robots systems,computer vision}, number = 4, pages = {6528--6535}, timestamp = {2023-05-10T16:23:27.000+0200}, title = {{Autonomous Tissue Retraction in Robotic Assisted Minimally Invasive Surgery - A Feasibility Study}}, volume = 5, year = 2020 }