Publications
87 results found
You J, Ajlouni S, Kakaletri I, et al., 2024, XRelevanceCAM: towards explainable tissue characterization with improved localisation of pathological structures in probe-based confocal laser endomicroscopy., Int J Comput Assist Radiol Surg
PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) enables intraoperative tissue characterization with improved resection rates of brain tumours. Although a plethora of deep learning models have been developed for automating tissue characterization, their lack of transparency is a concern. To tackle this issue, techniques like Class Activation Map (CAM) and its variations highlight image regions related to model decisions. However, they often fall short of providing human-interpretable visual explanations for surgical decision support, primarily due to the shattered gradient problem or insufficient theoretical underpinning. METHODS: In this paper, we introduce XRelevanceCAM, an explanation method rooted in a better backpropagation approach, incorporating sensitivity and conservation axioms. This enhanced method offers greater theoretical foundation and effectively mitigates the shattered gradient issue when compared to other CAM variants. RESULTS: Qualitative and quantitative evaluations are based on ex vivo pCLE data of brain tumours. XRelevanceCAM effectively highlights clinically relevant areas that characterize the tissue type. Specifically, it yields a remarkable 56% improvement over our closest baseline, RelevanceCAM, in the network's shallowest layer as measured by the mean Intersection over Union (mIoU) metric based on ground-truth annotations (from 18 to 28.07%). Furthermore, a 6% improvement in mIoU is observed when generating the final saliency map from all network layers. CONCLUSION: We introduce a new CAM variation, XRelevanceCAM, for precise identification of clinically important structures in pCLE data. This can aid introperative decision support in brain tumour resection surgery, as validated in our performance study.
Dyck M, Weld A, Klodmann J, et al., 2024, Toward Safe and Collaborative Robotic Ultrasound Tissue Scanning in Neurosurgery, IEEE Transactions on Medical Robotics and Bionics, Vol: 6, Pages: 64-67
Intraoperative ultrasound imaging is used to facilitate safe brain tumour resection. However, due to challenges with image interpretation and the physical scanning, this tool has yet to achieve widespread adoption in neurosurgery. In this paper, we introduce the components and workflow of a novel, versatile robotic platform for intraoperative ultrasound tissue scanning in neurosurgery. An RGB-D camera attached to the robotic arm allows for automatic object localisation with ArUco markers, and 3D surface reconstruction as a triangular mesh using the ImFusion Suite software solution. Impedance controlled guidance of the US probe along arbitrary surfaces, represented as a mesh, enables collaborative US scanning, i.e., autonomous, teleoperated and hands-on guided data acquisition. A preliminary experiment evaluates the suitability of the conceptual workflow and system components for probe landing on a custom-made soft-tissue phantom. Further assessment in future experiments will be necessary to prove the effectiveness of the presented platform.
Cartucho J, Weld A, Tukra S, et al., 2024, SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery., Med Image Anal, Vol: 91
This paper introduces the "SurgT: Surgical Tracking" challenge which was organized in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). There were two purposes for the creation of this challenge: (1) the establishment of the first standardized benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, have been provided. Participants were assigned the task of developing algorithms to track the movement of soft tissues, represented by bounding boxes, in stereo endoscopic videos. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge, to verify the efficacy of unsupervised deep learning algorithms in tracking soft-tissue. The metric used for ranking the methods was the Expected Average Overlap (EAO) score, which measures the average overlap between a tracker's and the ground truth bounding boxes. Coming first in the challenge was the deep learning submission by ICVS-2Ai with a superior EAO score of 0.617. This method employs ARFlow to estimate unsupervised dense optical flow from cropped images, using photometric and regularization losses. Second, Jmees with an EAO of 0.583, uses deep learning for surgical tool segmentation on top of a non-deep learning baseline method: CSRT. CSRT by itself scores a similar EAO of 0.563. The results from this challenge show that currently, non-deep learning methods are still competitive. The dataset and benchmarking tool created for this challenge have been made publicly available at https://surgt.grand-challenge.org/. This challenge is expected to contribute to the develo
Tukra S, Xu H, Xu C, et al., 2023, Generalizable stereo depth estimation with masked image modelling, Healthcare Technology Letters, ISSN: 2053-3713
Generalizable and accurate stereo depth estimation is vital for 3D reconstruction, especially in surgery. Supervised learning methods obtain best performance however, limited ground truth data for surgical scenes limits generalizability. Self-supervised methods don't need ground truth, but suffer from scale ambiguity and incorrect disparity prediction due to inconsistency of photometric loss. This work proposes a two-phase training procedure that is generalizable and retains the high performance of supervised methods. It entails: (1) performing self-supervised representation learning of left and right views via masked image modelling (MIM) to learn generalizable semantic stereo features (2) utilizing the MIM pre-trained model to learn robust depth representation via supervised learning for disparity estimation on synthetic data only. To improve stereo representations learnt via MIM, perceptual loss terms are introduced, which improve the model's stereo representations learnt by explicitly encouraging the learning of higher scene-level features. Qualitative and quantitative performance evaluation on surgical and natural scenes shows that the approach achieves sub-millimetre accuracy and lowest errors respectively, setting a new state-of-the-art. Despite not training on surgical nor natural scene data for disparity estimation.
Huang B, Hu Y, Nguyen A, et al., 2023, Detecting the sensing area of a laparoscopic probe in minimally invasive cancer surgery, MICCAI 2023, Publisher: Springer Nature Switzerland, Pages: 260-270, ISSN: 0302-9743
In surgical oncology, it is challenging for surgeons to identify lymph nodes and completely resect cancer even with pre-operative imaging systems like PET and CT, because of the lack of reliable intraoperative visualization tools. Endoscopic radio-guided cancer detection and resection has recently been evaluated whereby a novel tethered laparoscopic gamma detector is used to localize a preoperatively injected radiotracer. This can both enhance the endoscopic imaging and complement preoperative nuclear imaging data. However, gamma activity visualization is challenging to present to the operator because the probe is non-imaging and it does not visibly indicate the activity origination on the tissue surface. Initial failed attempts used segmentation or geometric methods, but led to the discovery that it could be resolved by leveraging high-dimensional image features and probe position information. To demonstrate the effectiveness of this solution, we designed and implemented a simple regression network that successfully addressed the problem. To further validate the proposed solution, we acquired and publicly released two datasets captured using a custom-designed, portable stereo laparoscope system. Through intensive experimentation, we demonstrated that our method can successfully and effectively detect the sensing area, establishing a new performance benchmark. Code and data are available at https://github.com/br0202/Sensing_area_detection.git.
Roddan A, Xu C, Ajlouni S, et al., 2023, Explainable image classification with improved trustworthiness for tissue characterisation, MICCAI 2023, Publisher: Springer Nature Switzerland, Pages: 575-585, ISSN: 0302-9743
The deployment of Machine Learning models intraoperatively for tissue characterisation can assist decision making and guide safe tumour resections. For the surgeon to trust the model, explainability of the generated predictions needs to be provided. For image classification models, pixel attribution (PA) and risk estimation are popular methods to infer explainability. However, the former method lacks trustworthiness while the latter can not provide visual explanation of the model’s attention. In this paper, we propose the first approach which incorporates risk estimation into a PA method for improved and more trustworthy image classification explainability. The proposed method iteratively applies a classification model with a PA method to create a volume of PA maps. We introduce a method to generate an enhanced PA map by estimating the expectation values of the pixel-wise distributions. In addition, the coefficient of variation (CV) is used to estimate pixel-wise risk of this enhanced PA map. Hence, the proposed method not only provides an improved PA map but also produces an estimation of risk on the output PA values. Performance evaluation on probe-based Confocal Laser Endomicroscopy (pCLE) data verifies that our improved explainability method outperforms the state-of-the-art.
Cui Z, Cartucho J, Giannarou S, et al., 2023, Caveats on the First-Generation da Vinci Research Kit: Latent Technical Constraints and Essential Calibrations, IEEE ROBOTICS & AUTOMATION MAGAZINE, ISSN: 1070-9932
Xu H, Runciman M, Cartucho J, et al., 2023, Graph-based pose estimation of texture-less surgical tools for autonomous robot control, 2023 IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 2731-2737
In Robot-assisted Minimally Invasive Surgery (RMIS), the estimation of the pose of surgical tools is crucial for applications such as surgical navigation, visual servoing, autonomous robotic task execution and augmented reality. A plethora of hardware-based and vision-based methods have been proposed in the literature. However, direct application of these methods to RMIS has significant limitations due to partial tool visibility, occlusions and changes in the surgical scene. In this work, a novel keypoint-graph-based network is proposed to estimate the pose of texture-less cylindrical surgical tools of small diameter. To deal with the challenges in RMIS, keypoint object representation is used and for the first time, temporal information is combined with spatial information in keypoint graph representation, for keypoint refinement. Finally, stable and accurate tool pose is computed using a PnP solver. Our performance evaluation study has shown that the proposed method is able to accurately predict the pose of a textureless robotic shaft with an ADD-S score of over 98%. The method outperforms state-of-the-art pose estimation models under challenging conditions such as object occlusion and changes in the lighting of the scene.
Weld A, Cartucho J, Xu C, et al., 2023, Regularising disparity estimation via multi task learning with structured light reconstruction, COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION, Vol: 11, Pages: 1206-1214, ISSN: 2168-1163
Weld A, Agrawal A, Giannarou S, 2023, Ultrasound segmentation using a 2D UNet with Bayesian volumetric support, MICCAI 2022 25th International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer Nature Switzerland, Pages: 63-68, ISSN: 0302-9743
We present a novel 2D segmentation neural network design for the segmentation of tumour tissue in intraoperative ultrasound (iUS). Due to issues with brain shift and tissue deformation, pre-operative imaging for tumour resection has limited reliability within the operating room (OR). iUS serves as a tool for improving tumour localisation and boundary delineation. Our proposed method takes inspiration from Bayesian networks. Rather than using a conventional 3D UNet, we develop a technique which samples from the volume around the query slice, and perform multiple segmentation’s which provides volumetric support to improve the accuracy of the segmentation of the query slice. Our results show that our proposed architecture achieves an 0.04 increase in the validation dice score compared to the benchmark network.
Wang C, Cartucho J, Elson D, et al., 2022, Towards autonomous control of surgical instruments using adaptive-fusion tracking and robot self-calibration, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 2395-2401, ISSN: 2153-0858
The ability to track surgical instruments in realtime is crucial for autonomous Robotic Assisted Surgery (RAS). Recently, the fusion of visual and kinematic data has been proposed to track surgical instruments. However, these methods assume that both sensors are equally reliable, and cannot successfully handle cases where there are significant perturbations in one of the sensors' data. In this paper, we address this problem by proposing an enhanced fusion-based method. The main advantage of our method is that it can adjust fusion weights to adapt to sensor perturbations and failures. Another problem is that before performing an autonomous task, these robots have to be repetitively recalibrated by a human for each new patient to estimate the transformations between the different robotic arms. To address this problem, we propose a self-calibration algorithm that empowers the robot to autonomously calibrate the transformations by itself in the beginning of the surgery. We applied our fusion and selfcalibration algorithms for autonomous ultrasound tissue scanning and we showed that the robot achieved stable ultrasound imaging when using our method. Our performance evaluation shows that our proposed method outperforms the state-of-art both in normal and challenging situations.
Huang B, Zheng J-Q, Nguyen A, et al., 2022, Self-supervised depth estimation in laparoscopic image using 3D geometric consistency, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 13-22, ISSN: 0302-9743
Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system. Since per-pixel depth ground truth is difficult to acquire for laparoscopic image data, it is rarely possible to apply supervised depth estimation to surgical applications. As an alternative, self-supervised methods have been introduced to train depth estimators using only synchronized stereo image pairs. However, most recent work focused on the left-right consistency in 2D and ignored valuable inherent 3D information on the object in real world coordinates, meaning that the left-right 3D geometric structural consistency is not fully utilized. To overcome this limitation, we present M3Depth, a self-supervised depth estimator to leverage 3D geometric structural information hidden in stereo pairs while keeping monocular inference. The method also removes the influence of border regions unseen in at least one of the stereo images via masking, to enhance the correspondences between left and right images in overlapping areas. Extensive experiments show that our method outperforms previous self-supervised approaches on both a public dataset and a newly acquired dataset by a large margin, indicating a good generalization across different samples and laparoscopes.
DeLorey C, Davids JD, Cartucho J, et al., 2022, A cable‐driven soft robotic end‐effector actuator for probe‐based confocal laser endomicroscopy: Development and preclinical validation, Translational Biophotonics, ISSN: 2627-1850
Huang B, Zheng J-Q, Giannarou S, et al., 2022, H-Net: unsupervised attention-based stereo depth estimation leveraging epipolar geometry, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 4459-4466
Depth estimation from a stereo image pair has become one of the most explored applications in computer vision, with most previous methods relying on fully supervised learning settings. However, due to the difficulty in acquiring accurate and scalable ground truth data, the training of fully supervised methods is challenging. As an alternative, self-supervised methods are becoming more popular to mitigate this challenge. In this paper, we introduce the H-Net, a deep-learning framework for unsupervised stereo depth estimation that leverages epipolar geometry to refine stereo matching. For the first time, a Siamese autoencoder architecture is used for depth estimation which allows mutual information between rectified stereo images to be extracted. To enforce the epipolar constraint, the mutual epipolar attention mechanism has been designed which gives more emphasis to correspondences of features that lie on the same epipolar line while learning mutual information between the input stereo pair. Stereo correspondences are further enhanced by incorporating semantic information to the proposed attention mechanism. More specifically, the optimal transport algorithm is used to suppress attention and eliminate outliers in areas not visible in both cameras. Extensive experiments on KITTI2015 and Cityscapes show that the proposed modules are able to improve the performance of the unsupervised stereo depth estimation methods while closing the gap with the fully supervised approaches.
Tukra S, Giannarou S, 2022, Randomly connected neural networks for self-supervised monocular depth estimation, COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION, Vol: 10, Pages: 390-399, ISSN: 2168-1163
Huang B, Nguyen A, Wang S, et al., 2022, Simultaneous depth estimation and surgical tool segmentation in laparoscopic images, IEEE Transactions on Medical Robotics and Bionics, Vol: 4, Pages: 335-338, ISSN: 2576-3202
Surgical instrument segmentation and depth estimation are crucial steps to improve autonomy in robotic surgery. Most recent works treat these problems separately, making the deployment challenging. In this paper, we propose a unified framework for depth estimation and surgical tool segmentation in laparoscopic images. The network has an encoder-decoder architecture and comprises two branches for simultaneously performing depth estimation and segmentation. To train the network end to end, we propose a new multi-task loss function that effectively learns to estimate depth in an unsupervised manner, while requiring only semi-ground truth for surgical tool segmentation. We conducted extensive experiments on different datasets to validate these findings. The results showed that the end-to-end network successfully improved the state-of-the-art for both tasks while reducing the complexity during their deployment.
Maier-Hein L, Eisenmann M, Sarikaya D, et al., 2022, Surgical data science-from concepts toward clinical translation, MEDICAL IMAGE ANALYSIS, Vol: 76, ISSN: 1361-8415
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- Citations: 49
Huang B, Tuch D, Vyas K, et al., 2022, Self-supervised monocular laparoscopic images depth estimation leveraging interactive closest point in 3D to enable image-guided radioguided surgery, European Molecular Imaging Meeting
Xu C, Roddan A, Davids J, et al., 2022, Deep Regression with Spatial-Frequency Feature Coupling and Image Synthesis for Robot-Assisted Endomicroscopy, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 157-166, ISSN: 0302-9743
Tukra S, Giannarou S, 2022, Stereo Depth Estimation via Self-supervised Contrastive Representation Learning, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 604-614, ISSN: 0302-9743
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- Citations: 1
Cartucho J, Wang C, Huang B, et al., 2021, An enhanced marker pattern that achieves improved accuracy in surgical tool tracking, Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, Vol: 10, Pages: 1-9, ISSN: 2168-1163
In computer assisted interventions (CAI), surgical tool tracking is crucial for applications such as surgical navigation, surgical skill assessment, visual servoing, and augmented reality. Tracking of cylindrical surgical tools can be achieved by printing and attaching a marker to their shaft. However, the tracking error of existing cylindrical markers is still in the millimetre range, which is too large for applications such as neurosurgery requiring sub-millimetre accuracy. To achieve tool tracking with sub-millimetre accuracy, we designed an enhanced marker pattern, which is captured on images from a monocular laparoscopic camera. The images are used as input for a tracking method which is described in this paper. Our tracking method was compared to the state-of-the-art, on simulation and ex vivo experiments. This comparison shows that our method outperforms the current state-of-the-art. Our marker achieves a mean absolute error of 0.28 [mm] and 0.45 [°] on ex vivo data, and 0.47 [mm] and 1.46 [°] on simulation. Our tracking method is real-time and runs at 55 frames per second for 720×576 image resolution.
Huang B, Zheng J-Q, Nguyen A, et al., 2021, Self-supervised generative adverrsarial network for depth estimation in laparoscopic images, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer, Pages: 227-237
Dense depth estimation and 3D reconstruction of a surgical scene are crucial steps in computer assisted surgery. Recent work has shown that depth estimation from a stereo image pair could be solved with convolutional neural networks. However, most recent depth estimation models were trained on datasets with per-pixel ground truth. Such data is especially rare for laparoscopic imaging, making it hard to apply supervised depth estimation to real surgical applications. To overcome this limitation, we propose SADepth, a new self-supervised depth estimation method based on Generative Adversarial Networks. It consists of an encoder-decoder generator and a discriminator to incorporate geometry constraints during training. Multi-scale outputs from the generator help to solve the local minima caused by the photometric reprojection loss, while the adversarial learning improves the framework generation quality. Extensive experiments on two public datasets show that SADepth outperforms recent state-of-the-art unsupervised methods by a large margin, and reduces the gap between supervised and unsupervised depth estimation in laparoscopic images.
Davids J, Makariou S-G, Ashrafian H, et al., 2021, Automated Vision-Based Microsurgical Skill Analysis in Neurosurgery Using Deep Learning: Development and Preclinical Validation, WORLD NEUROSURGERY, Vol: 149, Pages: E669-E686, ISSN: 1878-8750
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- Citations: 12
Berthet-Rayne P, Sadati S, Petrou G, et al., 2021, MAMMOBOT: A Miniature Steerable Soft Growing Robot for Early Breast Cancer Detection, IEEE Robotics and Automation Letters, Pages: 1-1
Collins JW, Marcus HJ, Ghazi A, et al., 2021, Ethical implications of AI in robotic surgical training: A Delphi consensus statement, European Urology Focus, ISSN: 2405-4569
ContextAs the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.ObjectivesTo provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee.Evidence acquisitionThe project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement.Evidence synthesisThere was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI.ConclusionsUsing the Delphi methodology, we achieved international consensus among experts to develop and reach
Tukra S, Marcus HJ, Giannarou S, 2021, See-Through Vision with Unsupervised Scene Occlusion Reconstruction., IEEE Trans Pattern Anal Mach Intell, Vol: PP
Among the greatest of the challenges of Minimally Invasive Surgery (MIS) is the inadequate visualisation of the surgical field through keyhole incisions. Moreover, occlusions caused by instruments or bleeding can completely obfuscate anatomical landmarks, reduce surgical vision and lead to iatrogenic injury. The aim of this paper is to propose an unsupervised end-to-end deep learning framework, based on Fully Convolutional Neural networks to reconstruct the view of the surgical scene under occlusions and provide the surgeon with intraoperative see-through vision in these areas. A novel generative densely connected encoder-decoder architecture has been designed which enables the incorporation of temporal information by introducing a new type of 3D convolution, the so called 3D partial convolution, to enhance the learning capabilities of the network and fuse temporal and spatial information. To train the proposed framework, a unique loss function has been proposed which combines perceptual, reconstruction, style, temporal and adversarial loss terms, for generating high fidelity image reconstructions. Advancing the state-of-the-art, our method can reconstruct the underlying view obstructed by irregularly shaped occlusions of divergent size, location and orientation. The proposed method has been validated on in-vivo MIS video data, as well as natural scenes on a range of occlusion-to-image (OIR) ratios.
Bautista-Salinas D, Kundrat D, Kogkas A, et al., 2021, Integrated Augmented Reality Feedback for Cochlear Implant Surgery Instruments, IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, Vol: 3, Pages: 261-264
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- Citations: 2
Vieira Cartucho J, Wang C, Huang B, et al., 2021, An Enhanced Marker Pattern that Achieves Improved Accuracy in Surgical Tool Tracking, Joint MICCAI 2021 Workshop on Augmented Environments for Computer-Assisted Interventions (AE-CAI), Computer-Assisted Endoscopy (CARE) and Context-Aware Operating Theatres 2.0 (OR2.0), Publisher: Taylor and Francis, ISSN: 2168-1163
Cartucho J, Tukra S, Li Y, et al., 2021, VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol: 9, Pages: 331-338, ISSN: 2168-1163
Surgical robots rely on robust and efficient computer vision algorithms to be able to intervene in real-time. The main problem, however, is that the training or testing of such algorithms, especially when using deep learning techniques, requires large endoscopic datasets which are challenging to obtain, since they require expensive hardware, ethical approvals, patient consent and access to hospitals. This paper presents VisionBlender, a solution to efficiently generate large and accurate endoscopic datasets for validating surgical vision algorithms. VisionBlender is a synthetic dataset generator that adds a user interface to Blender, allowing users to generate realistic video sequences with ground truth maps of depth, disparity, segmentation masks, surface normals, optical flow, object pose, and camera parameters. VisionBlender was built with special focus on robotic surgery, and examples of endoscopic data that can be generated using this tool are presented. Possible applications are also discussed, and here we present one of those applications where the generated data has been used to train and evaluate state-of-the-art 3D reconstruction algorithms. Being able to generate realistic endoscopic datasets efficiently, VisionBlender promises an exciting step forward in robotic surgery.
Davids J, Manivannan S, Darzi A, et al., 2020, Simulation for skills training in neurosurgery: a systematic review, meta-analysis, and analysis of progressive scholarly acceptance., Neurosurgical Review, Vol: 44, Pages: 1853-1867, ISSN: 0344-5607
At a time of significant global unrest and uncertainty surrounding how the delivery of clinical training will unfold over the coming years, we offer a systematic review, meta-analysis, and bibliometric analysis of global studies showing the crucial role simulation will play in training. Our aim was to determine the types of simulators in use, their effectiveness in improving clinical skills, and whether we have reached a point of global acceptance. A PRISMA-guided global systematic review of the neurosurgical simulators available, a meta-analysis of their effectiveness, and an extended analysis of their progressive scholarly acceptance on studies meeting our inclusion criteria of simulation in neurosurgical education were performed. Improvement in procedural knowledge and technical skills was evaluated. Of the identified 7405 studies, 56 studies met the inclusion criteria, collectively reporting 50 simulator types ranging from cadaveric, low-fidelity, and part-task to virtual reality (VR) simulators. In all, 32 studies were included in the meta-analysis, including 7 randomised controlled trials. A random effects, ratio of means effects measure quantified statistically significant improvement in procedural knowledge by 50.2% (ES 0.502; CI 0.355; 0.649, p < 0.001), technical skill including accuracy by 32.5% (ES 0.325; CI - 0.482; - 0.167, p < 0.001), and speed by 25% (ES - 0.25, CI - 0.399; - 0.107, p < 0.001). The initial number of VR studies (n = 91) was approximately double the number of refining studies (n = 45) indicating it is yet to reach progressive scholarly acceptance. There is strong evidence for a beneficial impact of adopting simulation in the improvement of procedural knowledge and technical skill. We show a growing trend towards the adoption of neurosurgical simulators, although we have not fully gained progressive scholarly acceptance for VR-based simulation technologies in neurosurgical education.
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