Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery

The potential of Augmented Reality (AR) technology to assist minimally invasive surgeries (MIS) lies in its computational performanceand accuracy in dealing with challenging MIS scenes. Even with the latest hardware and software technologies, achieving both real-timeand accurate augmented information overlay in MIS is still a formidable task. In this paper, we present a novel real-time AR frameworkfor MIS that achieves interactive geometric aware augmented reality in endoscopic surgery with stereo views. Our framework tracks themovement of the endoscopic camera and simultaneously reconstructs a dense geometric mesh of the MIS scene. The movement of the camerais predicted by minimising the re-projection error to achieve a fast tracking performance, while the 3D mesh is incrementally built by a densezero mean normalised cross correlation stereo matching method to improve the accuracy of the surface reconstruction. Our proposed systemdoes not require any prior template or pre-operative scan and can infer the geometric information intra-operatively in real-time. With thegeometric information available, our proposed AR framework is able to interactively add annotations, localisation of tumors and vessels,and measurement labeling with greater precision and accuracy compared with the state of the art approaches

Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery

Purpose: Single-incision laparoscopic surgery decreases postoperative infections, but introduces limitations in the surgeon’s maneuverability and in the surgical field of view. This work aims at enhancing intra-operative surgical visualization by exploiting the 3D information about the surgical site. An interactive guidance system is proposed wherein the pose of preoperative tissue models is updated online. A critical process involves the intra-operative acquisition of tissue surfaces. It can be achieved using stereoscopic imaging and 3D reconstruction techniques. This work contributes to this process by proposing new methods for improved dense 3D reconstruction of soft tissues, which allows a more accurate deformation identification and facilitates the registration process. Methods: Two methods for soft tissue 3D reconstruction are proposed: Method 1 follows the traditional approach of the block matching algorithm. Method 2 performs a nonparametric modified census transform to be more robust to illumination variation. The simple linear iterative clustering (SLIC) super-pixel algorithm is exploited for disparity refinement by filling holes in the disparity images. Results: The methods were validated using two video datasets from the Hamlyn Centre, achieving an accuracy of 2.95 and 1.66 mm, respectively. A comparison with ground-truth data demonstrated the disparity refinement procedure: (1) increases the number of reconstructed points by up to 43% and (2) does not affect the accuracy of the 3D reconstructions significantly. Conclusion: Both methods give results that compare favorably with the state-of-the-art methods. The computational time constraints their applicability in real time, but can be greatly improved by using a GPU implementation

Tracking and Mapping in Medical Computer Vision: A Review

As computer vision algorithms are becoming more capable, their applications in clinical systems will become more pervasive. These applications include diagnostics such as colonoscopy and bronchoscopy, guiding biopsies and minimally invasive interventions and surgery, automating instrument motion and providing image guidance using pre-operative scans. Many of these applications depend on the specific visual nature of medical scenes and require designing and applying algorithms to perform in this environment. In this review, we provide an update to the field of camera-based tracking and scene mapping in surgery and diagnostics in medical computer vision. We begin with describing our review process, which results in a final list of 515 papers that we cover. We then give a high-level summary of the state of the art and provide relevant background for those who need tracking and mapping for their clinical applications. We then review datasets provided in the field and the clinical needs therein. Then, we delve in depth into the algorithmic side, and summarize recent developments, which should be especially useful for algorithm designers and to those looking to understand the capability of off-the-shelf methods. We focus on algorithms for deformable environments while also reviewing the essential building blocks in rigid tracking and mapping since there is a large amount of crossover in methods. Finally, we discuss the current state of the tracking and mapping methods along with needs for future algorithms, needs for quantification, and the viability of clinical applications in the field. We conclude that new methods need to be designed or combined to support clinical applications in deformable environments, and more focus needs to be put into collecting datasets for training and evaluation.Comment: 31 pages, 17 figure

EnViSoRS: Enhanced Vision System for Robotic Surgery. A User-Defined Safety Volume Tracking to Minimize the Risk of Intraoperative Bleeding

open6siIn abdominal surgery, intra-operative bleeding is one of the major complications that affect the outcome of minimally invasive surgical procedures. One of the causes is attributed to accidental damages to arteries or veins, and one of the possible risk factors falls on the surgeon's skills. This paper presents the development and application of an Enhanced Vision System for Robotic Surgery (EnViSoRS), based on a user-defined Safety Volume (SV) tracking to minimise the risk of intra-operative bleeding. It aims at enhancing the surgeon's capabilities by providing Augmented Reality (AR) assistance towards the protection of vessels from injury during the execution of surgical procedures with a robot. The core of the framework consists in: (i) a hybrid tracking algorithm (LT-SAT tracker) that robustly follows a user-defined Safety Area (SA) in long term; (ii) a dense soft tissue 3D reconstruction algorithm, necessary for the computation of the SV; (iii) AR features for visualisation of the SV to be protected and of a graphical gauge indicating the current distance between the instruments and the reconstructed surface. EnViSoRS was integrated with a commercial robotic surgery system (the dVRK system) for testing and validation. The experiments aimed at demonstrating the accuracy, robustness, performance and usability of EnViSoRS during the execution of a simulated surgical task on a liver phantom. Results show an overall accuracy in accordance with surgical requirements (< 5mm), and high robustness in the computation of the SV in terms of precision and recall of its identification. The optimisation strategy implemented to speed up the computational time is also described and evaluated, providing AR features update rate up to 4 fps without impacting the real-time visualisation of the stereo endoscopic video. Finally, qualitative results regarding the system usability indicate that the proposed system integrates well with the commercial surgical robot and has indeed potential to offer useful assistance during real surgeries.openPenza, Veronica; De Momi, Elena; Enayati, Nima; Chupin, Thibaud; Ortiz, Jesús; Mattos, Leonardo S.Penza, Veronica; DE MOMI, Elena; Enayati, Nima; Chupin, THIBAUD JEAN EUDES; Ortiz, Jesús; Mattos, Leonardo S

A comprehensive survey on recent deep learning-based methods applied to surgical data

Minimally invasive surgery is highly operator dependant with a lengthy procedural time causing fatigue to surgeon and risks to patients such as injury to organs, infection, bleeding, and complications of anesthesia. To mitigate such risks, real-time systems are desired to be developed that can provide intra-operative guidance to surgeons. For example, an automated system for tool localization, tool (or tissue) tracking, and depth estimation can enable a clear understanding of surgical scenes preventing miscalculations during surgical procedures. In this work, we present a systematic review of recent machine learning-based approaches including surgical tool localization, segmentation, tracking, and 3D scene perception. Furthermore, we provide a detailed overview of publicly available benchmark datasets widely used for surgical navigation tasks. While recent deep learning architectures have shown promising results, there are still several open research problems such as a lack of annotated datasets, the presence of artifacts in surgical scenes, and non-textured surfaces that hinder 3D reconstruction of the anatomical structures. Based on our comprehensive review, we present a discussion on current gaps and needed steps to improve the adaptation of technology in surgery.Comment: This paper is to be submitted to International journal of computer visio