Towards safer robot-assisted surgery: A markerless augmented reality framework.

Robot-assisted surgery is rapidly developing in the medical field, and the integration of augmented reality shows the potential to improve the operation performance of surgeons by providing more visual information. In this paper, we proposed a markerless augmented reality framework to enhance safety by avoiding intra-operative bleeding, which is a high risk caused by collision between surgical instruments and delicate blood vessels (arteries or veins). Advanced stereo reconstruction and segmentation networks are compared to find the best combination to reconstruct the intra-operative blood vessel in 3D space for registration with the pre-operative model, and the minimum distance detection between the instruments and the blood vessel is implemented. A robot-assisted lymphadenectomy is emulated on the da Vinci Research Kit in a dry lab, and ten human subjects perform this operation to explore the usability of the proposed framework. The result shows that the augmented reality framework can help the users to avoid the dangerous collision between the instruments and the delicate blood vessel while not introducing an extra load. It provides a flexible framework that integrates augmented reality into the medical robotic platform to enhance safety during surgery.

Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments.

The robotics discipline is exploring precise and versatile solutions for upper-limb rehabilitation in Multiple Sclerosis (MS). People with MS can greatly benefit from robotic systems to help combat the complexities of this disease, which can impair the ability to perform activities of daily living (ADLs). In order to present the potential and the limitations of smart mechatronic devices in the mentioned clinical domain, this review is structured to propose a concise SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of robotic rehabilitation in MS. Through the SWOT Analysis, a method mostly adopted in business management, this paper addresses both internal and external factors that can promote or hinder the adoption of upper-limb rehabilitation robots in MS. Subsequently, it discusses how the synergy with another category of interaction technologies - the systems underlying virtual and augmented environments - may empower Strengths, overcome Weaknesses, expand Opportunities, and handle Threats in rehabilitation robotics for MS. The impactful adaptability of these digital settings (extensively used in rehabilitation for MS, even to approach ADL-like tasks in safe simulated contexts) is the main reason for presenting this approach to face the critical issues of the aforementioned SWOT Analysis. This methodological proposal aims at paving the way for devising further synergistic strategies based on the integration of medical robotic devices with other promising technologies to help upper-limb functional recovery in MS.

Path tracking control of a steerable catheter in transcatheter cardiology interventions.

PURPOSE: Intracardiac transcatheter interventions allow for reducing trauma and hospitalization stays as compared to standard surgery. In the treatment of mitral regurgitation, the most widely adopted transcatheter approach consists in deploying a clip on the mitral valve leaflets by means of a catheter that is run through veins from a peripheral access to the left atrium. However, precise manipulation of the catheter from outside the body while copying with the path constraints imposed by the vessels remains challenging. METHODS: We proposed a path tracking control framework that provides adequate motion commands to the robotic steerable catheter for autonomous navigation through vascular lumens. The proposed work implements a catheter kinematic model featuring nonholonomic constraints. Relying on the real-time measurements from an electromagnetic sensor and a fiber Bragg grating sensor, a two-level feedback controller was designed to control the catheter. RESULTS: The proposed method was tested in a patient-specific vessel phantom. A median position error between the center line of the vessel and the catheter tip trajectory was found to be below 2 mm, with a maximum error below 3 mm. Statistical testing confirmed that the performance of the proposed method exhibited no significant difference in both free space and the contact region. CONCLUSION: The preliminary in vitro studies presented in this paper showed promising accuracy in navigating the catheter within the vessel. The proposed approach enables autonomous control of a steerable catheter for transcatheter cardiology interventions without the request of calibrating the intuitive parameters or acquiring a training dataset.

Learning-based keypoint registration for fetoscopic mosaicking.

PURPOSE: In twin-to-twin transfusion syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. In the current practice, TTTS is treated surgically by closing abnormal anastomoses using laser ablation. This surgery is minimally invasive and relies on fetoscopy. Limited field of view makes anastomosis identification a challenging task for the surgeon. METHODS: To tackle this challenge, we propose a learning-based framework for in vivo fetoscopy frame registration for field-of-view expansion. The novelties of this framework rely on a learning-based keypoint proposal network and an encoding strategy to filter (i) irrelevant keypoints based on fetoscopic semantic image segmentation and (ii) inconsistent homographies. RESULTS: We validate our framework on a dataset of six intraoperative sequences from six TTTS surgeries from six different women against the most recent state-of-the-art algorithm, which relies on the segmentation of placenta vessels. CONCLUSION: The proposed framework achieves higher performance compared to the state of the art, paving the way for robust mosaicking to provide surgeons with context awareness during TTTS surgery.

Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training.

BACKGROUND AND OBJECTIVE: Safety of robotic surgery can be enhanced through augmented vision or artificial constraints to the robotl motion, and intra-operative depth estimation is the cornerstone of these applications because it provides precise position information of surgical scenes in 3D space. High-quality depth estimation of endoscopic scenes has been a valuable issue, and the development of deep learning provides more possibility and potential to address this issue. METHODS: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes. To this aim, a fully 3D encoder-decoder network integrating spatio-temporal layers is designed, and it adopts hierarchical prediction and progressive learning to enhance prediction accuracy and shorten training time. RESULTS: Our network gets the depth estimation accuracy of MAE 2.55±1.51 (mm) and RMSE 5.23±1.40 (mm) using 8 surgical videos with a resolution of 1280×1024, which performs better compared with six other state-of-the-art methods that were trained on the same data. CONCLUSIONS: Our network can implement a promising depth estimation performance in intra-operative scenes using stereo images, allowing the integration in robot-assisted surgery to enhance safety.

Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings.

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field.

Human-Centered Functional Task Design for Robotic Upper-Limb Rehabilitation.

Robotic rehabilitation has demonstrated slight positive effects compared to traditional care, but there is still a lack of targeted high-level control strategies in the current state-of-the-art for minimizing pathological motor behaviors. In this study, we analyzed upper-limb motion capture data from healthy subjects performing a pick-and-place task to identify task-specific variability in postural patterns. The results revealed consistent behaviors among subjects, presenting an opportunity to develop a novel extraction method for variable volume references based solely on observations from healthy individuals. These human-centered references were tested on a simulated 4 degrees-of-freedom upper-limb exoskeleton, showing its compliant adaptation to the path considering the variance in healthy subjects' motor behavior.

Toward a navigation framework for fetoscopy.

PURPOSE: Fetoscopic laser photocoagulation of placental anastomoses is the most effective treatment for twin-to-twin transfusion syndrome (TTTS). A robust mosaic of placenta and its vascular network could support surgeons' exploration of the placenta by enlarging the fetoscope field-of-view. In this work, we propose a learning-based framework for field-of-view expansion from intra-operative video frames. METHODS: While current state of the art for fetoscopic mosaicking builds upon the registration of anatomical landmarks which may not always be visible, our framework relies on learning-based features and keypoints, as well as robust transformer-based image-feature matching, without requiring any anatomical priors. We further address the problem of occlusion recovery and frame relocalization, relying on the computed features and their descriptors. RESULTS: Experiments were conducted on 10 in-vivo TTTS videos from two different fetal surgery centers. The proposed framework was compared with several state-of-the-art approaches, achieving higher [Formula: see text] on 7 out of 10 videos and a success rate of [Formula: see text] in occlusion recovery. CONCLUSION: This work introduces a learning-based framework for placental mosaicking with occlusion recovery from intra-operative videos using a keypoint-based strategy and features. The proposed framework can compute the placental panorama and recover even in case of camera tracking loss where other methods fail. The results suggest that the proposed framework has large potential to pave the way to creating a surgical navigation system for TTTS by providing robust field-of-view expansion.

FRSR: Framework for real-time scene reconstruction in robot-assisted minimally invasive surgery.

3D reconstruction of the intra-operative scenes provides precise position information which is the foundation of various safety related applications in robot-assisted surgery, such as augmented reality. Herein, a framework integrated into a known surgical system is proposed to enhance the safety of robotic surgery. In this paper, we present a scene reconstruction framework to restore the 3D information of the surgical site in real time. In particular, a lightweight encoder-decoder network is designed to perform disparity estimation, which is the key component of the scene reconstruction framework. The stereo endoscope of da Vinci Research Kit (dVRK) is adopted to explore the feasibility of the proposed approach, and it provides the possibility for the migration to other Robot Operating System (ROS) based robot platforms due to the strong independence on hardware. The framework is evaluated using three different scenarios, including a public dataset (3018 pairs of endoscopic images), the scene from the dVRK endoscope in our lab as well as a self-made clinical dataset captured from an oncology hospital. Experimental results show that the proposed framework can reconstruct 3D surgical scenes in real time (25 FPS), and achieve high accuracy (2.69 ± 1.48 mm in MAE, 5.47 ± 1.34 mm in RMSE and 0.41 ± 0.23 in SRE, respectively). It demonstrates that our framework can reconstruct intra-operative scenes with high reliability of both accuracy and speed, and the validation of clinical data also shows its potential in surgery. This work enhances the state of art in 3D intra-operative scene reconstruction based on medical robot platforms. The clinical dataset has been released to promote the development of scene reconstruction in the medical image community.

Semi-Supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images.

OBJECTIVE: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. METHOD: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. CONCLUSION: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. SIGNIFICANCE: This study shows the potential of using semi-supervised GAN-based bladder tissue classification when annotations are limited in multi-domain data.

Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.

PURPOSE: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation. METHODS: In this paper, we tailored the 3D ResNet to predict the OS of patients with PCNSL. To overcome the limitation of data sparsity, we introduced data augmentation and transfer learning, and we evaluated the results using r stratified k-fold cross-validation. To explain the results of our model, gradient-weighted class activation mapping was applied. RESULTS: We obtained the best performance (the standard error) on post-contrast T1-weighted (T1Gd)-area under curve [Formula: see text], accuracy [Formula: see text], precision [Formula: see text], recall [Formula: see text] and F1-score [Formula: see text], while compared with ML-based models on clinical data and radiomics data, respectively, further confirming the stability of our model. Also, we observed that PCNSL is a whole-brain disease and in the cases where the OS is less than 1 year, it is more difficult to distinguish the tumor boundary from the normal part of the brain, which is consistent with the clinical outcome. CONCLUSIONS: All these findings indicate that T1Gd can improve prognosis predictions of patients with PCNSL. To the best of our knowledge, this is the first time to use DL to explain model patterns in OS classification of patients with PCNSL. Future work would involve collecting more data of patients with PCNSL, or additional retrospective studies on different patient populations with rare diseases, to further promote the clinical role of our model.

Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients.

Primary Central Nervous System Lymphoma (PCNSL) is an aggressive neoplasm with a poor prognosis. Although therapeutic progresses have significantly improved Overall Survival (OS), a number of patients do not respond to HD-MTX-based chemotherapy (15-25%) or experience relapse (25-50%) after an initial response. The reasons underlying this poor response to therapy are unknown. Thus, there is an urgent need to develop improved predictive models for PCNSL. In this study, we investigated whether radiomics features can improve outcome prediction in patients with PCNSL. A total of 80 patients diagnosed with PCNSL were enrolled. A patient sub-group, with complete Magnetic Resonance Imaging (MRI) series, were selected for the stratification analysis. Following radiomics feature extraction and selection, different Machine Learning (ML) models were tested for OS and Progression-free Survival (PFS) prediction. To assess the stability of the selected features, images from 23 patients scanned at three different time points were used to compute the Interclass Correlation Coefficient (ICC) and to evaluate the reproducibility of each feature for both original and normalized images. Features extracted from Z-score normalized images were significantly more stable than those extracted from non-normalized images with an improvement of about 38% on average (p-value < 10-12). The area under the ROC curve (AUC) showed that radiomics-based prediction overcame prediction based on current clinical prognostic factors with an improvement of 23% for OS and 50% for PFS, respectively. These results indicate that radiomics features extracted from normalized MR images can improve prognosis stratification of PCNSL patients and pave the way for further study on its potential role to drive treatment choice.

FUN-SIS: A Fully UNsupervised approach for Surgical Instrument Segmentation.

Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art approaches completely rely on the availability of a ground-truth supervision signal, obtained via manual annotation, thus expensive to collect at large scale. In this paper, we present FUN-SIS, a Fully-UNsupervised approach for binary Surgical Instrument Segmentation. FUN-SIS trains a per-frame segmentation model on completely unlabelled endoscopic videos, by solely relying on implicit motion information and instrument shape-priors. We define shape-priors as realistic segmentation masks of the instruments, not necessarily coming from the same dataset/domain as the videos. The shape-priors can be collected in various and convenient ways, such as recycling existing annotations from other datasets. We leverage them as part of a novel generative-adversarial approach, allowing to perform unsupervised instrument segmentation of optical-flow images during training. We then use the obtained instrument masks as pseudo-labels in order to train a per-frame segmentation model; to this aim, we develop a learning-from-noisy-labels architecture, designed to extract a clean supervision signal from these pseudo-labels, leveraging their peculiar noise properties. We validate the proposed contributions on three surgical datasets, including the MICCAI 2017 EndoVis Robotic Instrument Segmentation Challenge dataset. The obtained fully-unsupervised results for surgical instrument segmentation are almost on par with the ones of fully-supervised state-of-the-art approaches. This suggests the tremendous potential of the proposed method to leverage the great amount of unlabelled data produced in the context of minimally invasive surgery.

Position-based dynamics simulator of vessel deformations for path planning in robotic endovascular catheterization.

A major challenge during autonomous navigation in endovascular interventions is the complexity of operating in a deformable but constrained workspace with an instrument. Simulation of deformations for it can provide a cost-effective training platform for path planning. Aim of this study is to develop a realistic, auto-adaptive, and visually plausible simulator to predict vessels' global deformation induced by the robotic catheter's contact and cyclic heartbeat motion. Based on a Position-based Dynamics (PBD) approach for vessel modeling, Particle Swarm Optimization (PSO) algorithm is employed for an auto-adaptive calibration of PBD deformation parameters and of the vessels movement due to a heartbeat. In-vitro experiments were conducted and compared with in-silico results. The end-user evaluation results were reported through quantitative performance metrics and a 5-Point Likert Scale questionnaire. Compared with literature, this simulator has an error of 0.23±0.13% for deformation and 0.30±0.85mm for the aortic root displacement. In-vitro experiments show an error of 1.35±1.38mm for deformation prediction. The end-user evaluation results show that novices are more accustomed to using joystick controllers, and cardiologists are more satisfied with the visual authenticity. The real-time and accurate performance of the simulator make this framework suitable for creating a dynamic environment for autonomous navigation of robotic catheters.

Lesion segmentation in lung CT scans using unsupervised adversarial learning.

Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments.

Validation of SART 3.5D algorithm for cerebrovascular dynamics and artery versus vein classification in presurgical 3D digital subtraction angiographies.

Classification of arteries and veins in cerebral angiograms can increase the safety of neurosurgical procedures, such as StereoElectroEncephaloGraphy, and aid the diagnosis of vascular pathologies, as arterovenous malformations. We propose a new method for vessel classification using the contrast medium dynamics in rotational digital subtraction angiography (DSA). After 3D DSA and angiogram segmentation, contrast enhanced projections are processed to suppress soft tissue and bone structures attenuation effect and further enhance the CM flow. For each voxel labelled as vessel, a time intensity curve (TIC) is obtained as a linear combination of temporal basis functions whose weights are addressed by simultaneous algebraic reconstruction technique (SART 3.5D), expanded to include dynamics. Each TIC is classified by comparing the areas under the curve in the arterial and venous phases. Clustering is applied to optimize the classification thresholds. On a dataset of 60 patients, a median value of sensitivity (90%), specificity (91%), and accuracy (92%) were obtained with respect to annotated arterial and venous voxels up to branching order 4-5. Qualitative results are also presented about CM arrival time mapping and its distribution in arteries and veins respectively. In conclusion, this study shows a valuable impact, at no protocol extra-cost or invasiveness, concerning surgical planning related to the enhancement of arteries as major organs at risk. Also, it opens a new scope on the pathophysiology of cerebrovascular dynamics and its anatomical relationships.

Robot-assisted ex vivo neobladder reconstruction: preliminary results of surgical skill evaluation.

PURPOSE: Advanced developments in the medical field have gradually increased the public demand for surgical skill evaluation. However, this assessment always depends on the direct observation of experienced surgeons, which is time-consuming and variable. The introduction of robot-assisted surgery provides a new possibility for this evaluation paradigm. This paper aims at evaluating surgeon performance automatically with novel evaluation metrics based on different surgical data. METHODS: Urologists ([Formula: see text]) from a hospital were requested to perform a simplified neobladder reconstruction on an ex vivo setup twice with different camera modalities ([Formula: see text]) randomly. They were divided into novices and experts ([Formula: see text], respectively) according to their experience in robot-assisted surgeries. Different performance metrics ([Formula: see text]) are proposed to achieve the surgical skill evaluation, considering both instruments and endoscope. Also, nonparametric tests are adopted to check if there are significant differences when evaluating surgeons performance. RESULTS: When grouping according to four stages of neobladder reconstruction, statistically significant differences can be appreciated in phase 1 ([Formula: see text]) and phase 2 ([Formula: see text]) with normalized time-related metrics and camera movement-related metrics, respectively. On the other hand, considering experience grouping shows that both metrics are able to highlight statistically significant differences between novice and expert performances in the control protocol. It also shows that the camera-related performance of experts is significantly different ([Formula: see text]) when handling the endoscope manually and when it is automatic. CONCLUSION: Surgical skill evaluation, using the approach in this paper, can effectively measure surgical procedures of surgeons with different experience. Preliminary results demonstrate that different surgical data can be fully utilized to improve the reliability of surgical evaluation. It also demonstrates its versatility and potential in the quantitative assessment of various surgical operations.

EMG-driven control in lower limb prostheses: a topic-based systematic review.

BACKGROUND: The inability of users to directly and intuitively control their state-of-the-art commercial prosthesis contributes to a low device acceptance rate. Since Electromyography (EMG)-based control has the potential to address those inabilities, research has flourished on investigating its incorporation in microprocessor-controlled lower limb prostheses (MLLPs). However, despite the proposed benefits of doing so, there is no clear explanation regarding the absence of a commercial product, in contrast to their upper limb counterparts. OBJECTIVE AND METHODOLOGIES: This manuscript aims to provide a comparative overview of EMG-driven control methods for MLLPs, to identify their prospects and limitations, and to formulate suggestions on future research and development. This is done by systematically reviewing academical studies on EMG MLLPs. In particular, this review is structured by considering four major topics: (1) type of neuro-control, which discusses methods that allow the nervous system to control prosthetic devices through the muscles; (2) type of EMG-driven controllers, which defines the different classes of EMG controllers proposed in the literature; (3) type of neural input and processing, which describes how EMG-driven controllers are implemented; (4) type of performance assessment, which reports the performance of the current state of the art controllers. RESULTS AND CONCLUSIONS: The obtained results show that the lack of quantitative and standardized measures hinders the possibility to analytically compare the performances of different EMG-driven controllers. In relation to this issue, the real efficacy of EMG-driven controllers for MLLPs have yet to be validated. Nevertheless, in anticipation of the development of a standardized approach for validating EMG MLLPs, the literature suggests that combining multiple neuro-controller types has the potential to develop a more seamless and reliable EMG-driven control. This solution has the promise to retain the high performance of the currently employed non-EMG-driven controllers for rhythmic activities such as walking, whilst improving the performance of volitional activities such as task switching or non-repetitive movements. Although EMG-driven controllers suffer from many drawbacks, such as high sensitivity to noise, recent progress in invasive neural interfaces for prosthetic control (bionics) will allow to build a more reliable connection between the user and the MLLPs. Therefore, advancements in powered MLLPs with integrated EMG-driven control have the potential to strongly reduce the effects of psychosomatic conditions and musculoskeletal degenerative pathologies that are currently affecting lower limb amputees.

Learning intraoperative organ manipulation with context-based reinforcement learning.

PURPOSE: Automation of sub-tasks during robotic surgery is challenging due to the high variability of the surgical scenes intra- and inter-patients. For example, the pick and place task can be executed different times during the same operation and for distinct purposes. Hence, designing automation solutions that can generalise a skill over different contexts becomes hard. All the experiments are conducted using the Pneumatic Attachable Flexible (PAF) rail, a novel surgical tool designed for robotic-assisted intraoperative organ manipulation. METHODS: We build upon previous open-source surgical Reinforcement Learning (RL) training environment to develop a new RL framework for manipulation skills, rlman. In rlman, contextual RL agents are trained to solve different aspects of the pick and place task using the PAF rail system. rlman is implemented to support both low- and high-dimensional state information to solve surgical sub-tasks in a simulation environment. RESULTS: We use rlman to train state of the art RL agents to solve four different surgical sub-tasks involving manipulation skills using the PAF rail. We compare the results with state-of-the-art benchmarks found in the literature. We evaluate the ability of the agent to be able to generalise over different aspects of the targeted surgical environment. CONCLUSION: We have shown that the rlman framework can support the training of different RL algorithms for solving surgical sub-task, analysing the importance of context information for generalisation capabilities. We are aiming to deploy the trained policy on the real da Vinci using the dVRK and show that the generalisation of the trained policy can be transferred to the real world.

Real-time vessel segmentation and reconstruction for virtual fixtures for an active handheld microneurosurgical instrument.

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. RESULTS: We 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 [Formula: see text]m outside and 400 [Formula: see text]m inside the vessel. Micron's tip entered the forbidden region with a median penetration of just 8.84 [Formula: see text]m and 9.63 [Formula: see text]m, compared to 148.74 [Formula: see text]m and 117.17 [Formula: see text]m 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.