Abstract:Twin–twin transfusion syndrome requires interventional treatment using a fetoscopically introduced laser to sever the shared blood supply between the fetuses. This is a delicate procedure relying on small instrumentation with limited articulation to guide the laser tip and a narrow field of view to visualize all relevant vascular connections. In this letter, we report on a mechatronic design for a comanipulated instrument that combines concentric tube actuation to a larger manipulator constrained by a remote c… Show more
“…This will require to develop robust underwater registration methods adapted to intended applicative context (e.g learning-based approaches) or combine it with robotic imaging [8]. It will also be interesting to evaluate such a framework for deep underwater imaging as [2] observed that the thin refractive plane assumption well approximate for thick refractive interface.…”
Section: Resultsmentioning
confidence: 99%
“…The interface to camera centre distance along the camera's axis is denoted as d. An image point i observed in view j is denoted p [2,2], [2,4], [3,5], [4,3], [4,7], [5,6], [5,8], [6,9]) = 1.…”
“…This will require to develop robust underwater registration methods adapted to intended applicative context (e.g learning-based approaches) or combine it with robotic imaging [8]. It will also be interesting to evaluate such a framework for deep underwater imaging as [2] observed that the thin refractive plane assumption well approximate for thick refractive interface.…”
Section: Resultsmentioning
confidence: 99%
“…The interface to camera centre distance along the camera's axis is denoted as d. An image point i observed in view j is denoted p [2,2], [2,4], [3,5], [4,3], [4,7], [5,6], [5,8], [6,9]) = 1.…”
“…This limits the camera motion to only 4DoF (3 in rotation and 1 in translation) and results in illconditioned hand-eye constraints. While a possible solution would be to allow a surgical robot to freely move in 6DoF during a calibration phase, this is neither practical nor possible for mechanisms with mechanical RCM implementations [3]. However, as we will show in this paper, it is possible to take advantage of the RCM constraints to improve hand-eye calibration for such robot configurations.…”
Section: Trocar Pointmentioning
confidence: 99%
“…The schematic shows a magnified version of the type of movement of the camera when being used in RMIS. RCM is denoted at the trocar point to minimise a chance of a robot arm damaging the surrounding tissues [2], [3]. The camera motion is restricted around the RCM and this provides a very small motion range which is not sufficient for a decent calibration.…”
In the eye-in-hand robot configuration, hand-eye calibration plays a vital role in completing the link between the robot and camera coordinate systems. Calibration algorithms are mature and provide accurate transformation estimations for an effective camera-robot link but rely on a sufficiently wide range of calibration data to avoid errors and degenerate configurations. This can be difficult in the context of keyhole surgical robots because they are mechanically constrained to move around a remote centre of motion (RCM) which is located at the trocar port. The trocar limits the range of feasible calibration poses that can be obtained and results in ill-conditioned hand-eye constraints. In this paper, we propose a new approach to deal with this problem by incorporating the RCM constraints into the hand-eye formulation. We show that this not only avoids illconditioned constraints but is also more accurate than classic hand-eye calibration with a free 6DoF motion, due to solving simpler equations that take advantage of the reduced DoF. We validate our method using simulation to test numerical stability and a physical implementation on an RCM constrained KUKA LBR iiwa 14 R820 equipped with a NanEye stereo camera.
“…With the reduction in size, complex actuation systems are required. Long kinematic chains (12 active joints in the da Vinci robot [5], Intuitive Surgical, USA), micro-machined super-elastic tool guides with pneumatic artificial muscles [6] and concentric tubes [7] are recent examples of these highly complex actuation mechanisms. As a consequence, the kinematics of such robotic manipulators become less stable due to hysteresis, friction and backlash.…”
Abstract-Real-time tool segmentation from endoscopic videos is an essential part of many computer-assisted robotic surgical systems and of critical importance in robotic surgical data science. We propose two novel deep learning architectures for automatic segmentation of non-rigid surgical instruments. Both methods take advantage of automated deep-learningbased multi-scale feature extraction while trying to maintain an accurate segmentation quality at all resolutions. The two proposed methods encode the multi-scale constraint inside the network architecture. The first proposed architecture enforces it by cascaded aggregation of predictions and the second proposed network does it by means of a holistically-nested architecture where the loss at each scale is taken into account for the optimization process. As the proposed methods are for realtime semantic labeling, both present a reduced number of parameters. We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization of the network while maintaining the segmentation accuracy. We compare the proposed architectures against state-of-the-art fully convolutional networks. We validate our methods using existing benchmark datasets, including ex vivo cases with phantom tissue and different robotic surgical instruments present in the scene. Our results show a statistically significant improved Dice Similarity Coefficient over previous instrument segmentation methods. We analyze our design choices and discuss the key drivers for improving accuracy.
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