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

Laparoscopic Video Analysis for Training and Image Guided Surgery

Automatic analysis of Minimally Invasive Surgical video has the potential to drive new solutions for alleviating needs of safe and reproducible training programs, objective and transparent evaluation systems and navigation tools to assist surgeons and improve patient safety. Surgical video is an always available source of information, which can be used without any additional intrusive hardware in the operating room. This paper is focused on surgical video analysis methods and techniques. It describes authors' contributions in two key aspects, the 3D reconstruction of the surgical field and the segmentation and tracking of tools and organs based on laparoscopic video images. Results are given to illustrate the potential of this field of research, like the calculi of the 3D position and orientation of a tool from its 2D image, or the translation of a preoperative resection plan into a hepatectomy surgical procedure using the shading information of the image. Research efforts are required to further develop these technologies in order to harness all the valuable information available in any video-based surgery

EndoAbS dataset: Endoscopic abdominal stereo image dataset for benchmarking 3D stereo reconstruction algorithms

Background: 3D reconstruction algorithms are of fundamental importance for augmented reality applications in computer-assisted surgery. However, few datasets of endoscopic stereo images with associated 3D surface references are currently openly available, preventing the proper validation of such algorithms. This work presents a new and rich dataset of endoscopic stereo images (EndoAbS dataset). Methods: The dataset includes (i) endoscopic stereo images of phantom abdominal organs, (ii) a 3D organ surface reference (RF) generated with a laser scanner and (iii) camera calibration parameters. A detailed description of the generation of the phantom and the camera–laser calibration method is also provided. Results: An estimation of the overall error in creation of the dataset is reported (camera–laser calibration error 0.43 mm) and the performance of a 3D reconstruction algorithm is evaluated using EndoAbS, resulting in an accuracy error in accordance with state-of-the-art results (<2 mm). Conclusions: The EndoAbS dataset contributes to an increase the number and variety of openly available datasets of surgical stereo images, including a highly accurate RF and different surgical conditions

Towards an Accurate Tracking of Liver Tumors for Augmented Reality in Robotic Assisted Surgery

International audienceThis article introduces a method for tracking the internal structures of the liver during robot-assisted procedures. Vascular network, tumors and cut planes, computed from pre-operative data, can be overlaid onto the laparoscopic view for image-guidance, even in the case of large motion or deformation of the organ. Compared to current methods, our method is able to precisely propagate surface motion to the internal structures. This is made possible by relying on a fast yet accurate biomechanical model of the liver combined with a robust visual tracking approach designed to properly constrain the model. Augmentation results are demonstrated on in-vivo sequences of a human liver during robotic surgery, while quantitative validation is performed on an ex-vivo porcine liver experimentation. Validation results show that our approach gives an accurate surface registration with an error of less than 6mm on the position of the tumor

EndoAbS dataset: Endoscopic abdominal stereo image dataset for benchmarking 3D stereo reconstruction algorithms

none5siembargoed_20190801Penza, Veronica; Ciullo, Andrea S.; Moccia, Sara; Mattos, Leonardo S.; De Momi, ElenaPenza, Veronica; Ciullo, Andrea S.; Moccia, Sara; Mattos, Leonardo S.; De Momi, Elen

Visual SLAM for Measurement and Augmented Reality in Laparoscopic Surgery

In spite of the great advances in laparoscopic surgery, this type of surgery still shows some difficulties during its realization, mainly caused by its complex maneuvers and, above all, by the loss of the depth perception. Unlike classical open surgery --laparotomy-- where surgeons have direct contact with organs and a complete 3D perception, laparoscopy is carried out by means of specialized instruments, and a monocular camera (laparoscope) in which the 3D scene is projected into a 2D plane --image. The main goal of this thesis is to face with this loss of depth perception by making use of Simultaneous Localization and Mapping (SLAM) algorithms developed in the fields of robotics and computer vision during the last years. These algorithms allow to localize, in real time (25 $\thicksim$ 30 frames per second), a camera that moves freely inside an unknown rigid environment while, at the same time, they build a map of this environment by exploiting images gathered by that camera. These algorithms have been extensively validated both in man-made environments (buildings, rooms, ...) and in outdoor environments, showing robustness to occlusions, sudden camera motions, or clutter. This thesis tries to extend the use of these algorithms to laparoscopic surgery. Due to the intrinsic nature of internal body images (they suffer from deformations, specularities, variable illumination conditions, limited movements, ...), applying this type of algorithms to laparoscopy supposes a real challenge. Knowing the camera (laparoscope) location with respect to the scene (abdominal cavity) and the 3D map of that scene opens new interesting possibilities inside the surgical field. This knowledge enables to do augmented reality annotations directly on the laparoscopic images (e.g. alignment of preoperative 3D CT models); intracavity 3D distance measurements; or photorealistic 3D reconstructions of the abdominal cavity recovering synthetically the lost depth. These new facilities provide security and rapidity to surgical procedures without disturbing the classical procedure workflow. Hence, these tools are available inside the surgeon's armory, being the surgeon who decides to use them or not. Additionally, knowledge of the camera location with respect to the patient's abdominal cavity is fundamental for future development of robots that can operate automatically since, knowing this location, the robot will be able to localize other tools controlled by itself with respect to the patient. In detail, the contributions of this thesis are: - To demonstrate the feasibility of applying SLAM algorithms to laparoscopy showing experimentally that using robust data association is a must. - To robustify one of these algorithms, in particular the monocular EKF-SLAM algorithm, by adapting a relocalization system and improving data association with a robust matching algorithm. - To develop of a robust matching method (1-Point RANSAC algorithm). - To develop a new surgical procedure to ease the use of visual SLAM in laparoscopy. - To make an extensive validation of the robust EKF-SLAM (EKF + relocalization + 1-Point RANSAC) obtaining millimetric errors and working in real time both on simulation and real human surgeries. The selected surgery has been the ventral hernia repair. - To demonstrate the potential of these algorithms in laparoscopy: they recover synthetically the depth of the operative field which is lost by using monocular laparoscopes, enable the insertion of augmented reality annotations, and allow to perform distance measurements using only a laparoscopic tool (to define the real scale) and laparoscopic images. - To make a clinical validation showing that these algorithms allow to shorten surgical times of operations and provide more security to the surgical procedures

Image-guided Simulation of Heterogeneous Tissue Deformation For Augmented Reality during Hepatic Surgery

International audienceThis paper presents a method for real-time augmentation of vas- cular network and tumors during minimally invasive liver surgery. Internal structures computed from pre-operative CT scans can be overlaid onto the laparoscopic view for surgery guidance. Com- pared to state-of-the-art methods, our method uses a real-time biomechanical model to compute a volumetric displacement field from partial three-dimensional liver surface motion. This permits to properly handle the motion of internal structures even in the case of anisotropic or heterogeneous tissues, as it is the case for the liver and many anatomical structures. Real-time augmentation results are presented on in vivo and ex vivo data and illustrate the benefits of such an approach for minimally invasive surgery

Preoperative liver registration for augmented monocular laparoscopy using backward–forward biomechanical simulation

PURPOSE: Augmented reality for monocular laparoscopy from a preoperative volume such as CT is achieved in two steps. The first step is to segment the organ in the preoperative volume and reconstruct its 3D model. The second step is to register the preoperative 3D model to an initial intraoperative laparoscopy image. To date, there does not exist an automatic initial registration method to solve the second step for the liver in the de facto operating room conditions of monocular laparoscopy. Existing methods attempt to solve for both deformation and pose simultaneously, leading to nonconvex problems with no optimal solution algorithms. METHODS: We propose in contrast to break the problem down into two parts, solving for (i) deformation and (ii) pose. Part (i) simulates biomechanical deformations from the preoperative to the intraoperative state to predict the liver’s unknown intraoperative shape by modeling gravity, the abdominopelvic cavity’s pressure and boundary conditions. Part (ii) rigidly registers the simulated shape to the laparoscopy image using contour cues. RESULTS: Our formulation leads to a well-posed problem, contrary to existing methods. This is because it exploits strong environment priors to complement the weak laparoscopic visual cues. CONCLUSION: Quantitative results with in silico and phantom experiments and qualitative results with laparosurgery images for two patients show that our method outperforms the state-of-the-art in accuracy and registration time

Ultrasound-Augmented Laparoscopy

Laparoscopic surgery is perhaps the most common minimally invasive procedure for many diseases in the abdomen. Since the laparoscopic camera provides only the surface view of the internal organs, in many procedures, surgeons use laparoscopic ultrasound (LUS) to visualize deep-seated surgical targets. Conventionally, the 2D LUS image is visualized in a display spatially separate from that displays the laparoscopic video. Therefore, reasoning about the geometry of hidden targets requires mentally solving the spatial alignment, and resolving the modality differences, which is cognitively very challenging. Moreover, the mental representation of hidden targets in space acquired through such cognitive medication may be error prone, and cause incorrect actions to be performed. To remedy this, advanced visualization strategies are required where the US information is visualized in the context of the laparoscopic video. To this end, efficient computational methods are required to accurately align the US image coordinate system with that centred in the camera, and to render the registered image information in the context of the camera such that surgeons perceive the geometry of hidden targets accurately. In this thesis, such a visualization pipeline is described. A novel method to register US images with a camera centric coordinate system is detailed with an experimental investigation into its accuracy bounds. An improved method to blend US information with the surface view is also presented with an experimental investigation into the accuracy of perception of the target locations in space

SERV-CT: A disparity dataset from cone-beam CT for validation of endoscopic 3D reconstruction

In computer vision, reference datasets from simulation and real outdoor scenes have been highly successful in promoting algorithmic development in stereo reconstruction. Endoscopic stereo reconstruction for surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surface properties and the presence of blood and smoke. These issues present difficulties for both stereo reconstruction itself and also for standardised dataset production. Previous datasets have been produced using computed tomography (CT) or structured light reconstruction on phantom or ex vivo models. We present a stereo-endoscopic reconstruction validation dataset based on cone-beam CT (SERV-CT). Two ex vivo small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Subsequent orientation of the endoscope was manually aligned to match the stereoscopic view and benchmark disparities, depths and occlusions are calculated. The requirement of a CT scan limited the number of stereo pairs to 8 from each ex vivo sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of around 2 pixels and a depth accuracy of about 2 mm. A simplified reference dataset is provided consisting of endoscope image pairs with corresponding calibration, disparities, depths and occlusions covering the majority of the endoscopic image and a range of tissue types, including smooth specular surfaces, as well as significant variation of depth. We assessed the performance of various stereo algorithms from online available repositories. There is a significant variation between algorithms, highlighting some of the challenges of surgical endoscopic images. The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths covering the majority of the endoscopic image. This complements existing resources well and we hope will aid the development of surgical endoscopic anatomical reconstruction algorithms