Registration-free simultaneous catheter and environment modelling

© Springer International Publishing AG 2016. Endovascular procedures are challenging to perform due to the complexity and difficulty in catheter manipulation. The simultaneous recovery of the 3D structure of the vasculature and the catheter position and orientation intra-operatively is necessary in catheter control and navigation. State-of-art Simultaneous Catheter and Environment Modelling provides robust and real-time 3D vessel reconstruction based on real-time intravascular ultrasound (IVUS) imaging and electromagnetic (EM) sensing,but still relies on accurate registration between EM and pre-operative data. In this paper,a registration-free vessel reconstruction method is proposed for endovascular navigation. In the optimisation framework,the EM-CT registration is estimated and updated intra-operatively together with the 3D vessel reconstruction from IVUS,EM and pre-operative data,and thus does not require explicit registration. The proposed algorithm can also deal with global (patient) motion and periodic deformation caused by cardiac motion. Phantom and invivo experiments validate the accuracy of the algorithm and the results demonstrate the potential clinical value of the technique

Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations

With recent advances in biophotonics, techniques such as narrow band imaging, confocal laser endomicroscopy, fluorescence spectroscopy, and optical coherence tomography, can be combined with normal white-light endoscopes to provide in vivo microscopic tissue characterisation, potentially avoiding the need for offline histological analysis. Despite the advantages of these techniques to provide online optical biopsy in situ, it is challenging for gastroenterologists to retarget the optical biopsy sites during endoscopic examinations. This is because optical biopsy does not leave any mark on the tissue. Furthermore, typical endoscopic cameras only have a limited field-of-view and the biopsy sites often enter or exit the camera view as the endoscope moves. In this paper, a framework for online tracking and retargeting is proposed based on the concept of tracking-by-detection. An online detection cascade is proposed where a random binary descriptor using Haar-like features is included as a random forest classifier. For robust retargeting, we have also proposed a RANSAC-based location verification component that incorporates shape context. The proposed detection cascade can be readily integrated with other temporal trackers. Detailed performance evaluation on in vivo gastrointestinal video sequences demonstrates the performance advantage of the proposed method over the current state-of-the-art

SCEM+: Real-Time Robust Simultaneous Catheter and Environment Modeling for Endovascular Navigation

© 2016 IEEE. Endovascular procedures are characterised by significant challenges mainly due to the complexity in catheter control and navigation. Real-time recovery of the 3-D structure of the vasculature is necessary to visualise the interaction between the catheter and its surrounding environment to facilitate catheter manipulations. State-of-the-art intraoperative vessel reconstruction approaches are increasingly relying on nonionising imaging techniques such as optical coherence tomography (OCT) and intravascular ultrasound (IVUS). To enable accurate recovery of vessel structures and to deal with sensing errors and abrupt catheter motions, this letter presents a robust and real-time vessel reconstruction scheme for endovascular navigation based on IVUS and electromagnetic (EM) tracking. It is formulated as a nonlinear optimisation problem, which considers the uncertainty in both the IVUS contour and the EM pose, as well as vessel morphology provided by preoperative data. Detailed phantom validation is performed and the results demonstrate the potential clinical value of the technique

Pulmonary vasospasm in systemic sclerosis: noninvasive techniques for detection

In a subgroup of patients with systemic sclerosis (SSc), vasospasm affecting the pulmonary circulation may contribute to worsening respiratory symptoms, including dyspnea. Noninvasive assessment of pulmonary blood flow (PBF), utilizing inert-gas rebreathing (IGR) and dual-energy computed-tomography pulmonary angiography (DE-CTPA), may be useful for identifying pulmonary vasospasm. Thirty-one participants (22 SSc patients and 9 healthy volunteers) underwent PBF assessment with IGR and DE-CTPA at baseline and after provocation with a cold-air inhalation challenge (CACh). Before the study investigations, participants were assigned to subgroups: group A included SSc patients who reported increased breathlessness after exposure to cold air (n = 11), group B included SSc patients without cold-air sensitivity (n = 11), and group C patients included the healthy volunteers. Median change in PBF from baseline was compared between groups A, B, and C after CACh. Compared with groups B and C, in group A there was a significant decline in median PBF from baseline at 10 minutes (−10%; range: −52.2% to 4.0%; P < 0.01), 20 minutes (−17.4%; −27.9% to 0.0%; P < 0.01), and 30 minutes (−8.5%; −34.4% to 2.0%; P < 0.01) after CACh. There was no significant difference in median PBF change between groups B or C at any time point and no change in pulmonary perfusion on DE-CTPA. Reduction in pulmonary blood flow following CACh suggests that pulmonary vasospasm may be present in a subgroup of patients with SSc and may contribute to worsening dyspnea on exposure to cold

H-Net: unsupervised attention-based stereo depth estimation leveraging epipolar geometry

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

Towards autonomous control of surgical instruments using adaptive-fusion tracking and robot self-calibration

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

Detecting the Sensing Area of A Laparoscopic Probe in Minimally Invasive Cancer Surgery

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.gitComment: Accepted by MICCAI 202

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

PURPOSE: Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. METHODS: The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. RESULTS: Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. CONCLUSION: ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments

Self-supervised generative adverrsarial network for depth estimation in laparoscopic images

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

Detecting the Sensing Area of a Laparoscopic Probe in Minimally Invasive Cancer Surgery

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