Long Term Safety Area Tracking (LT-SAT) with online failure detection and recovery for robotic minimally invasive surgery

partially_open6Despite the benefits introduced by robotic systems in abdominal Minimally Invasive Surgery (MIS), major complications can still affect the outcome of the procedure, such as intra-operative bleeding. One of the causes is attributed to accidental damages to arteries or veins by the surgical tools, and some of the possible risk factors are related to the lack of sub-surface visibilty. Assistive tools guiding the surgical gestures to prevent these kind of injuries would represent a relevant step towards safer clinical procedures. However, it is still challenging to develop computer vision systems able to fulfill the main requirements: (i) long term robustness, (ii) adaptation to environment/object variation and (iii) real time processing. The purpose of this paper is to develop computer vision algorithms to robustly track soft tissue areas (Safety Area, SA), defined intra-operatively by the surgeon based on the real-time endoscopic images, or registered from a pre-operative surgical plan. We propose a framework to combine an optical flow algorithm with a tracking-by-detection approach in order to be robust against failures caused by: (i) partial occlusion, (ii) total occlusion, (iii) SA out of the field of view, (iv) deformation, (v) illumination changes, (vi) abrupt camera motion, (vii), blur and (viii) smoke. A Bayesian inference-based approach is used to detect the failure of the tracker, based on online context information. A Model Update Strategy (MUpS) is also proposed to improve the SA re-detection after failures, taking into account the changes of appearance of the SA model due to contact with instruments or image noise. The performance of the algorithm was assessed on two datasets, representing ex-vivo organs and in-vivo surgical scenarios. Results show that the proposed framework, enhanced with MUpS, is capable of maintain high tracking performance for extended periods of time ( ≃ 4 min - containing the aforementioned events) with high precision (0.7) and recall (0.8) values, and with a recovery time after a failure between 1 and 8 frames in the worst case.openPenza, Veronica; Du, Xiaofei; Stoyanov, Danail; Forgione, Antonello; Mattos, Leonardo S; De Momi, ElenaPenza, Veronica; Du, Xiaofei; Stoyanov, DANAIL VALENTINOV; Forgione, Antonello; Mattos, Leonardo S; De Momi, Elen

Long Term Safety Area Tracking (LT-SAT) with online failure detection and recovery for robotic minimally invasive surgery.

Despite the benefits introduced by robotic systems in abdominal Minimally Invasive Surgery (MIS), major complications can still affect the outcome of the procedure, such as intra-operative bleeding. One of the causes is attributed to accidental damages to arteries or veins by the surgical tools, and some of the possible risk factors are related to the lack of sub-surface visibilty. Assistive tools guiding the surgical gestures to prevent these kind of injuries would represent a relevant step towards safer clinical procedures. However, it is still challenging to develop computer vision systems able to fulfill the main requirements: (i) long term robustness, (ii) adaptation to environment/object variation and (iii) real time processing. The purpose of this paper is to develop computer vision algorithms to robustly track soft tissue areas (Safety Area, SA), defined intra-operatively by the surgeon based on the real-time endoscopic images, or registered from a pre-operative surgical plan. We propose a framework to combine an optical flow algorithm with a tracking-by-detection approach in order to be robust against failures caused by: (i) partial occlusion, (ii) total occlusion, (iii) SA out of the field of view, (iv) deformation, (v) illumination changes, (vi) abrupt camera motion, (vii), blur and (viii) smoke. A Bayesian inference-based approach is used to detect the failure of the tracker, based on online context information. A Model Update Strategy (MUpS) is also proposed to improve the SA re-detection after failures, taking into account the changes of appearance of the SA model due to contact with instruments or image noise. The performance of the algorithm was assessed on two datasets, representing ex-vivo organs and in-vivo surgical scenarios. Results show that the proposed framework, enhanced with MUpS, is capable of maintain high tracking performance for extended periods of time ( ≃ 4 min - containing the aforementioned events) with high precision (0.7) and recall (0.8) values, and with a recovery time after a failure between 1 and 8 frames in the worst case

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

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialities. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to re-purpose decommissioned da Vinci surgical systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this paper, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain this initiative and build upon it

Visual Tracking in Robotic Minimally Invasive Surgery

Intra-operative imaging and robotics are some of the technologies driving forward better and more effective minimally invasive surgical procedures. To advance surgical practice and capabilities further, one of the key requirements for computationally enhanced interventions is to know how instruments and tissues move during the operation. While endoscopic video captures motion, the complex appearance dynamic effects of surgical scenes are challenging for computer vision algorithms to handle with robustness. Tackling both tissue and instrument motion estimation, this thesis proposes a combined non-rigid surface deformation estimation method to track tissue surfaces robustly and in conditions with poor illumination. For instrument tracking, a keypoint based 2D tracker that relies on the Generalized Hough Transform is developed to initialize a 3D tracker in order to robustly track surgical instruments through long sequences that contain complex motions. To handle appearance changes and occlusion a patch-based adaptive weighting with segmentation and scale tracking framework is developed. It takes a tracking-by-detection approach and a segmentation model is used to assigns weights to template patches in order to suppress back- ground information. The performance of the method is thoroughly evaluated showing that without any offline-training, the tracker works well even in complex environments. Finally, the thesis proposes a novel 2D articulated instrument pose estimation framework, which includes detection-regression fully convolutional network and a multiple instrument parsing component. The framework achieves compelling performance and illustrates interesting properties includ- ing transfer between different instrument types and between ex vivo and in vivo data. In summary, the thesis advances the state-of-the art in visual tracking for surgical applications for both tissue and instrument motion estimation. It contributes to developing the technological capability of full surgical scene understanding from endoscopic video

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

Hepatocellular Carcinoma

This open access book offers a comprehensive review of hepatocellular carcinoma (HCC) with a particular focus on the pathobiology and clinical aspects of the disease, including diagnosis and treatment. HCC is becoming one of the most common causes of cancer-related death worldwide. It is the fifth most common malignancy in men and the ninth in women, with an estimated 500,000 to 1 million new cases annually around the world. Independent of its cause, cirrhosis is considered a major clinical and histopathological risk factor for HCC development. Five percent of all cirrhotic patients develop HCC every year. Diagnostic tools for HCC include blood tests, high-quality imaging studies and liver biopsy. The treatment of HCC depends on the size and location of the HCC and includes surgical resection, liver transplantation, endovascular approaches, percutaneous ablation, and medical treatments. The book is organized into four parts – overview, diagnosis, management strategies, and recommendations – and aims to provide surgeons and clinicians with a valuable resource for complete and up-to-date research on the clinical aspects and management of HCC