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      Robotic Endoscope Control Via Autonomous Instrument Tracking

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          Abstract

          Many keyhole interventions rely on bi-manual handling of surgical instruments, forcing the main surgeon to rely on a second surgeon to act as a camera assistant. In addition to the burden of excessively involving surgical staff, this may lead to reduced image stability, increased task completion time and sometimes errors due to the monotony of the task. Robotic endoscope holders, controlled by a set of basic instructions, have been proposed as an alternative, but their unnatural handling may increase the cognitive load of the (solo) surgeon, which hinders their clinical acceptance. More seamless integration in the surgical workflow would be achieved if robotic endoscope holders collaborated with the operating surgeon via semantically rich instructions that closely resemble instructions that would otherwise be issued to a human camera assistant, such as “focus on my right-hand instrument.” As a proof of concept, this paper presents a novel system that paves the way towards a synergistic interaction between surgeons and robotic endoscope holders. The proposed platform allows the surgeon to perform a bimanual coordination and navigation task, while a robotic arm autonomously performs the endoscope positioning tasks. Within our system, we propose a novel tooltip localization method based on surgical tool segmentation and a novel visual servoing approach that ensures smooth and appropriate motion of the endoscope camera. We validate our vision pipeline and run a user study of this system. The clinical relevance of the study is ensured through the use of a laparoscopic exercise validated by the European Academy of Gynaecological Surgery which involves bi-manual coordination and navigation. Successful application of our proposed system provides a promising starting point towards broader clinical adoption of robotic endoscope holders.

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          U-Net: Convolutional Networks for Biomedical Image Segmentation

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            Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

            Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.
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              The Pascal Visual Object Classes Challenge: A Retrospective

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                Author and article information

                Contributors
                Journal
                Front Robot AI
                Front Robot AI
                Front. Robot. AI
                Frontiers in Robotics and AI
                Frontiers Media S.A.
                2296-9144
                11 April 2022
                2022
                : 9
                : 832208
                Affiliations
                [1] 1 Department of Mechanical Engineering , KU Leuven , Leuven, Belgium
                [2] 2 Department of Medical Physics and Biomedical Engineering , University College London , London, United Kingdom
                [3] 3 Department of Surgical and Interventional Engineering , King’s College London , London, United Kingdom
                [4] 4 Core Lab ROB, Flanders Make , Lommel, Belgium
                [5] 5 Department of Development and Regeneration , Division Woman and Child , KU Leuven , Leuven, Belgium
                Author notes

                Edited by: Daniele Cafolla, Mediterranean Neurological Institute Neuromed (IRCCS), Italy

                Reviewed by: Juan Sandoval, University of Poitiers, France

                Luigi Pavone, Mediterranean Neurological Institute Neuromed (IRCCS), Italy

                *Correspondence: Luis C. Garcia-Peraza-Herrera, luis_c.garcia_peraza_herrera@ 123456kcl.ac.uk
                [ † ]

                These authors have contributed equally to this work and share first authorship

                This article was submitted to Biomedical Robotics, a section of the journal Frontiers in Robotics and AI

                Article
                832208
                10.3389/frobt.2022.832208
                9035496
                514fea22-d364-43e2-a034-78e3e99640a6
                Copyright © 2022 Gruijthuijsen, Garcia-Peraza-Herrera, Borghesan, Reynaerts, Deprest, Ourselin, Vercauteren and Vander Poorten.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 December 2021
                : 17 February 2022
                Categories
                Robotics and AI
                Original Research

                minimally invasive surgery,endoscope holders,endoscope robots,endoscope control,visual servoing,instrument tracking

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