Microsurgical procedures, such as petroclival meningioma resection, require careful surgical actions in order to remove tumor tissue, while avoiding brain and vessel damaging. Such procedures are currently performed under microscope magnification. Robotic tools are emerging in order to filter surgeons' unintended movements and prevent tools from entering forbidden regions such as vascular structures. The present work investigates the use of a handheld robotic tool (Micron) to automate vessel avoidance in microsurgery. In particular, we focused on vessel segmentation, implementing a deep-learning-based segmentation strategy in microscopy images, and its integration with a feature-based passive 3D reconstruction algorithm to obtain accurate and robust vessel position. We then implemented a virtual-fixture-based strategy to control the handheld robotic tool and perform vessel avoidance. Clay vascular phantoms, lying on a background obtained from microscopy images recorded during petroclival meningioma surgery, were used for testing the segmentation and control algorithms. When testing the segmentation algorithm on 100 different phantom images, a median Dice similarity coefficient equal to 0.96 was achieved. A set of 25 Micron trials of 80 s in duration, each involving the interaction of Micron with a different vascular phantom, were recorded, with a safety distance equal to 2 mm, which was comparable to the median vessel diameter. Micron's tip entered the forbidden region 24% of the time when the control algorithm was active. However, the median penetration depth was 16.9 μm, which was two orders of magnitude lower than median vessel diameter. Results suggest the system can assist surgeons in performing safe vessel avoidance during neurosurgical procedures.
The integration of intra-operative sensors into surgical robots is a hot research topic since this can significantly facilitate complex surgical procedures by enhancing surgical awareness with real-time tissue information. However, currently available intra-operative sensing technologies are mainly based on image processing and force feedback, which normally require heavy computation or complicated hardware modifications of existing surgical tools. This paper presents the design and integration of electrical bio-impedance sensing into a commercial surgical robot tool, leading to the creation of a novel smart instrument that allows the identification of tissues by simply touching them. In addition, an advanced user interface is designed to provide guidance during the use of the system and to allow augmented-reality visualization of the tissue identification results. The proposed system imposes minor hardware modifications to an existing surgical tool, but adds the capability to provide a wealth of data about the tissue being manipulated. This has great potential to allow the surgeon (or an autonomous robotic system) to better understand the surgical environment. To evaluate the system, a series of ex-vivo experiments were conducted. The experimental results demonstrate that the proposed sensing system can successfully identify different tissue types with 100% classification accuracy. In addition, the user interface was shown to effectively and intuitively guide the user to measure the electrical impedance of the target tissue, presenting the identification results as augmented-reality markers for simple and immediate recognition.
Purpose Complications related to vascular damage such as intra-operative bleeding may be avoided during neurosurgical procedures such as petroclival meningioma surgery. To address this and improve the patient's safety, we designed a real-time blood vessel avoidance strategy that enables operation on deformable tissue during petroclival meningioma surgery using Micron, a handheld surgical robotic tool. Methods We integrated real-time intra-operative blood vessel segmentation of brain vasculature using deep learning, with a 3D reconstruction algorithm to obtain the vessel point cloud in real time. We then implemented a virtual-fixture-based strategy that prevented Micron's tooltip from entering a forbidden region around the vessel, thus avoiding damage to it. ResultsWe achieved a median Dice similarity coefficient of 0.97, 0.86, 0.87 and 0.77 on datasets of phantom blood vessels, petrosal vein, internal carotid artery and superficial vessels, respectively. We conducted trials with deformable clay vessel phantoms, keeping the forbidden region 400 μm outside and 400 μm inside the vessel. Micron's tip entered the forbidden region with a median penetration of just 8.84 μm and 9.63 μm, compared to 148.74 μm and 117.17 μm without our strategy, for the former and latter trials, respectively. Conclusion Real-time control of Micron was achieved at 33.3 fps. We achieved improvements in real-time segmentation of brain vasculature from intra-operative images and showed that our approach works even on non-stationary vessel phantoms. The results suggest that by enabling precise, real-time control, we are one step closer to using Micron in real neurosurgical procedures.
Figure 1. Schematic Description of the Proposed Method. Left: an arbitrary identity feature is selected and a mini-batch of vertices (Xij) is created by swapping features across different 3D shapes. Colours represent identities. Notice that features from the same identity have the same colour. Centre: a 3D-VAE ({E, G}) encodes Xij in its latent representations zij = (z f ij |z c ij ), which are subsequently decoded into X ij . In this case f corresponds to the nose. Therefore, while z f ij controls the shape of the nose, z c ij controls the shape of the rest of the face. Right: visual representation of all the different mesh features for which we seek to obtain a disentangled latent representation.
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