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

Automatic registration of 3D models to laparoscopic video images for guidance during liver surgery

Laparoscopic liver interventions offer significant advantages over open surgery, such as less pain and trauma, and shorter recovery time for the patient. However, they also bring challenges for the surgeons such as the lack of tactile feedback, limited field of view and occluded anatomy. Augmented reality (AR) can potentially help during laparoscopic liver interventions by displaying sub-surface structures (such as tumours or vasculature). The initial registration between the 3D model extracted from the CT scan and the laparoscopic video feed is essential for an AR system which should be efficient, robust, intuitive to use and with minimal disruption to the surgical procedure. Several challenges of registration methods in laparoscopic interventions include the deformation of the liver due to gas insufflation in the abdomen, partial visibility of the organ and lack of prominent geometrical or texture-wise landmarks. These challenges are discussed in detail and an overview of the state of the art is provided. This research project aims to provide the tools to move towards a completely automatic registration. Firstly, the importance of pre-operative planning is discussed along with the characteristics of the liver that can be used in order to constrain a registration method. Secondly, maximising the amount of information obtained before the surgery, a semi-automatic surface based method is proposed to recover the initial rigid registration irrespective of the position of the shapes. Finally, a fully automatic 3D-2D rigid global registration is proposed which estimates a global alignment of the pre-operative 3D model using a single intra-operative image. Moving towards incorporating the different liver contours can help constrain the registration, especially for partial surfaces. Having a robust, efficient AR system which requires no manual interaction from the surgeon will aid in the translation of such approaches to the clinics

Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays

In recent years, the entry into the market of self contained optical see-through headsets with integrated multi-sensor capabilities has led the way to innovative and technology driven augmented reality applications and has encouraged the adoption of these devices also across highly challenging medical and industrial settings. Despite this, the display calibration process of consumer level systems is still sub-optimal, particularly for those applications that require high accuracy in the spatial alignment between computer generated elements and a real-world scene. State-of-the-art manual and automated calibration procedures designed to estimate all the projection parameters are too complex for real application cases outside laboratory environments. This paper describes an off-line fast calibration procedure that only requires a camera to observe a planar pattern displayed on the see-through display. The camera that replaces the user’s eye must be placed within the eye-motion-box of the see-through display. The method exploits standard camera calibration and computer vision techniques to estimate the projection parameters of the display model for a generic position of the camera. At execution time, the projection parameters can then be refined through a planar homography that encapsulates the shift and scaling effect associated with the estimated relative translation from the old camera position to the current user’s eye position. Compared to classical SPAAM techniques that still rely on the human element and to other camera based calibration procedures, the proposed technique is flexible and easy to replicate in both laboratory environments and real-world settings

Global rigid registration of CT to video in laparoscopic liver surgery

PURPOSE: Image-guidance systems have the potential to aid in laparoscopic interventions by providing sub-surface structure information and tumour localisation. The registration of a preoperative 3D image with the intraoperative laparoscopic video feed is an important component of image guidance, which should be fast, robust and cause minimal disruption to the surgical procedure. Most methods for rigid and non-rigid registration require a good initial alignment. However, in most research systems for abdominal surgery, the user has to manually rotate and translate the models, which is usually difficult to perform quickly and intuitively. METHODS: We propose a fast, global method for the initial rigid alignment between a 3D mesh derived from a preoperative CT of the liver and a surface reconstruction of the intraoperative scene. We formulate the shape matching problem as a quadratic assignment problem which minimises the dissimilarity between feature descriptors while enforcing geometrical consistency between all the feature points. We incorporate a novel constraint based on the liver contours which deals specifically with the challenges introduced by laparoscopic data. RESULTS: We validate our proposed method on synthetic data, on a liver phantom and on retrospective clinical data acquired during a laparoscopic liver resection. We show robustness over reduced partial size and increasing levels of deformation. Our results on the phantom and on the real data show good initial alignment, which can successfully converge to the correct position using fine alignment techniques. Furthermore, since we can pre-process the CT scan before surgery, the proposed method runs faster than current algorithms. CONCLUSION: The proposed shape matching method can provide a fast, global initial registration, which can be further refined by fine alignment methods. This approach will lead to a more usable and intuitive image-guidance system for laparoscopic liver surgery

Advanced Calibration of Automotive Augmented Reality Head-Up Displays = Erweiterte Kalibrierung von Automotiven Augmented Reality-Head-Up-Displays

In dieser Arbeit werden fortschrittliche Kalibrierungsmethoden für Augmented-Reality-Head-up-Displays (AR-HUDs) in Kraftfahrzeugen vorgestellt, die auf parametrischen perspektivischen Projektionen und nichtparametrischen Verzerrungsmodellen basieren. Die AR-HUD-Kalibrierung ist wichtig, um virtuelle Objekte in relevanten Anwendungen wie z.B. Navigationssystemen oder Parkvorgängen korrekt zu platzieren. Obwohl es im Stand der Technik einige nützliche Ansätze für dieses Problem gibt, verfolgt diese Dissertation das Ziel, fortschrittlichere und dennoch weniger komplizierte Ansätze zu entwickeln. Als Voraussetzung für die Kalibrierung haben wir mehrere relevante Koordinatensysteme definiert, darunter die dreidimensionale (3D) Welt, den Ansichtspunkt-Raum, den HUD-Sichtfeld-Raum (HUD-FOV) und den zweidimensionalen (2D) virtuellen Bildraum. Wir beschreiben die Projektion der Bilder von einem AR-HUD-Projektor in Richtung der Augen des Fahrers als ein ansichtsabhängiges Lochkameramodell, das aus intrinsischen und extrinsischen Matrizen besteht. Unter dieser Annahme schätzen wir zunächst die intrinsische Matrix unter Verwendung der Grenzen des HUD-Sichtbereichs. Als nächstes kalibrieren wir die extrinsischen Matrizen an verschiedenen Blickpunkten innerhalb einer ausgewählten "Eyebox" unter Berücksichtigung der sich ändernden Augenpositionen des Fahrers. Die 3D-Positionen dieser Blickpunkte werden von einer Fahrerkamera verfolgt. Für jeden einzelnen Blickpunkt erhalten wir eine Gruppe von 2D-3D-Korrespondenzen zwischen einer Menge Punkten im virtuellen Bildraum und ihren übereinstimmenden Kontrollpunkten vor der Windschutzscheibe. Sobald diese Korrespondenzen verfügbar sind, berechnen wir die extrinsische Matrix am entsprechenden Betrachtungspunkt. Durch Vergleichen der neu projizierten und realen Pixelpositionen dieser virtuellen Punkte erhalten wir eine 2D-Verteilung von Bias-Vektoren, mit denen wir Warping-Karten rekonstruieren, welche die Informationen über die Bildverzerrung enthalten. Für die Vollständigkeit wiederholen wir die obigen extrinsischen Kalibrierungsverfahren an allen ausgewählten Betrachtungspunkten. Mit den kalibrierten extrinsischen Parametern stellen wir die Betrachtungspunkte wieder her im Weltkoordinatensystem. Da wir diese Punkte gleichzeitig im Raum der Fahrerkamera verfolgen, kalibrieren wir weiter die Transformation von der Fahrerkamera in den Weltraum unter Verwendung dieser 3D-3D-Korrespondenzen. Um mit nicht teilnehmenden Betrachtungspunkten innerhalb der Eyebox umzugehen, erhalten wir ihre extrinsischen Parameter und Warping-Karten durch nichtparametrische Interpolationen. Unsere Kombination aus parametrischen und nichtparametrischen Modellen übertrifft den Stand der Technik hinsichtlich der Zielkomplexität sowie Zeiteffizienz, während wir eine vergleichbare Kalibrierungsgenauigkeit beibehalten. Bei allen unseren Kalibrierungsschemen liegen die Projektionsfehler in der Auswertungsphase bei einer Entfernung von 7,5 Metern innerhalb weniger Millimeter, was einer Winkelgenauigkeit von ca. 2 Bogenminuten entspricht, was nahe am Auflösungvermögen des Auges liegt