Fluidic actuation for intra-operative in situ imaging

Devreker, Alain; Rosa, Benoît; Desjardins, Adrien E.; Alles, Erwin J.; Garcia-Peraza, L.C.; Maneas, Efthymios; Stoyanov, Danail; David, Anna L.; Vercauteren, Tom; Deprest, Jan; Ourselin, Sébastien; Reynaerts, Dominiek; Poorten, Emmanuel Vander

doi:10.1109/iros.2015.7353553

Cited by 22 publications

(17 citation statements)

References 21 publications

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“…The design of contracting devices in 3D presented in this section elucidates that contracting devices are similar to a segment of continuum robot actuated by PAMs and with an elastic backbone, such as Ref. 30 However, contracting devices integrate different parts and can be designed with the principles elucidated in this work to improve performance. Still, both contracting devices and devices including PAMs present the disadvantage of involving a protruding wall, which typically protrudes outward, requiring additional room to operate.…”

Section: Design Of Contracting Devicesmentioning

confidence: 97%

Toward a Common Framework for the Design of Soft Robotic Manipulators with Fluidic Actuation

2018

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Soft robotic manipulators with fluidic actuation are devices with easily deformable structures that comprise a set of chambers that can be pressurized to achieve structural deflection. These devices have experienced a rapid development in recent years, which is not least due to the advantages they offer in terms of robustness, affordability, and compliance. Nowadays, however, soft robotic manipulators are designed mostly by intuition, which complicates design improvement and hampers the advancement of the field. In this article, a general study of the design of soft robotic manipulators with fluidic actuation is presented using an analytical derivation. The study relies on a novel approach that is applicable to a general design and thus provides a common framework for the design of soft robots. In the study, two design layouts of interest are first justified, which correspond to extending and contracting devices. Design principles for each of the layouts are subsequently derived, both for planar and 3D scenarios, and considering operation to support any external loading and to provide any desired deflection. These principles are found to agree with the main design trends in the literature, although they also highlight the potential for improvement in specific aspects of the design geometry and stiffness distribution. The principles are used to identify the most suitable design for both extending and contracting devices in 2D and 3D and extract insight into their behavior. To showcase the use of these design principles, a prototypical scenario in minimally invasive surgery requiring a manipulator segment capable of bending in any direction is defined, where the objective is to maximize its lateral force. The principles are applied to determine the most suitable design. These also highlight the need for numerical analysis to optimize two design parameters. Finite element simulations are developed, and their results are reported. Among the most relevant is the fact that the cross-sectional area with pressurized fluid should be maximized and that the stiffness in the design should be selected to minimize structural stretching. The simulations yield the optimal design, which offers higher force than existing, reference ones. The simulations also provide verification for the study.

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Section: Design Of Contracting Devicesmentioning

confidence: 97%

Toward a Common Framework for the Design of Soft Robotic Manipulators with Fluidic Actuation

2018

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“…Ex vivo fetal surgery dataset consisting of 21 images for training and a video sequence of 10 seconds for testing. In both the images and the video a non-rigid McKibben artificial muscle [5] is actuated close to the surface of a human placenta. In order to prove the generalisation capabilities of the method, the training images were captured in air and the video was recorded under water, to facilitate different backgrounds and lighting conditions.…”

Section: … Frames Finalmentioning

confidence: 99%

“…Another application of tool segmentation is visual servoing of articulated or flexible surgical robots. As the dexterity of the instruments rises [5], it becomes increasingly difficult for the surgeon to understand the shape of these instruments. With the miniaturisation of said instruments, the kinematics of these devices become less deterministic due to effects from friction, hysteresis and backlash alongside with increased instrument compliance and safety.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Segmentation of Non-rigid Surgical Tools Based on Deep Learning and Tracking

García-Peraza-Herrera

Gruijthuijsen

et al. 2017

Lecture Notes in Computer Science

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Abstract. Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FCN) and optical flow tracking. Our method exploits the ability of deep neural networks to produce accurate segmentations of highly deformable parts along with the high speed of optical flow. Furthermore, the pre-trained FCN can be fine-tuned on a small amount of medical images without the need to hand-craft features. We validated our method using existing and new benchmark datasets, covering both ex vivo and in vivo real clinical cases where different surgical instruments are employed. Two versions of the method are presented, non-real-time and real-time. The former, using only deep learning, achieves a balanced accuracy of 89.6% on a real clinical dataset, outperforming the (non-real-time) state of the art by 3.8 percentage points. The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78.2% across all the validated datasets.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

ToolNet: Holistically-nested real-time segmentation of robotic surgical tools

García-Peraza-Herrera¹,

Li²,

Fidon³

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

128

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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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Fluidic actuation for intra-operative in situ imaging

Cited by 22 publications

References 21 publications

Toward a Common Framework for the Design of Soft Robotic Manipulators with Fluidic Actuation

Toward a Common Framework for the Design of Soft Robotic Manipulators with Fluidic Actuation

Real-Time Segmentation of Non-rigid Surgical Tools Based on Deep Learning and Tracking

ToolNet: Holistically-nested real-time segmentation of robotic surgical tools

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