Performance of image guided navigation in laparoscopic liver surgery – A systematic review

Background: Compared to open surgery, minimally invasive liver resection has improved short term outcomes. It is however technically more challenging. Navigated image guidance systems (IGS) are being developed to overcome these challenges. The aim of this systematic review is to provide an overview of their current capabilities and limitations. Methods: Medline, Embase and Cochrane databases were searched using free text terms and corresponding controlled vocabulary. Titles and abstracts of retrieved articles were screened for inclusion criteria. Due to the heterogeneity of the retrieved data it was not possible to conduct a meta-analysis. Therefore results are presented in tabulated and narrative format. Results: Out of 2015 articles, 17 pre-clinical and 33 clinical papers met inclusion criteria. Data from 24 articles that reported on accuracy indicates that in recent years navigation accuracy has been in the range of 8–15 mm. Due to discrepancies in evaluation methods it is difficult to compare accuracy metrics between different systems. Surgeon feedback suggests that current state of the art IGS may be useful as a supplementary navigation tool, especially in small liver lesions that are difficult to locate. They are however not able to reliably localise all relevant anatomical structures. Only one article investigated IGS impact on clinical outcomes. Conclusions: Further improvements in navigation accuracy are needed to enable reliable visualisation of tumour margins with the precision required for oncological resections. To enhance comparability between different IGS it is crucial to find a consensus on the assessment of navigation accuracy as a minimum reporting standard

Non-Rigid Liver Registration for Laparoscopy using Data-Driven Biomechanical Models

During laparoscopic liver resection, the limited access to the organ, the small field of view and lack of palpation can obstruct a surgeon’s workflow. Automatic navigation systems could use the images from preoperative volumetric organ scans to help the surgeons find their target (tumors) and risk-structures (vessels) more efficiently. This requires the preoperative data to be fused (or registered) with the intraoperative scene in order to display information at the correct intraoperative position. One key challenge in this setting is the automatic estimation of the organ’s current intra-operative deformation, which is required in order to predict the position of internal structures. Parameterizing the many patient-specific unknowns (tissue properties, boundary conditions, interactions with other tissues, direction of gravity) is very difficult. Instead, this work explores how to employ deep neural networks to solve the registration problem in a data-driven manner. To this end, convolutional neural networks are trained on synthetic data to estimate an organ’s intraoperative displacement field and thus its current deformation. To drive this estimation, visible surface cues from the intraoperative camera view must be supplied to the networks. Since reliable surface features are very difficult to find, the networks are adapted to also find correspondences between the pre- and intraoperative liver geometry automatically. This combines the search for correspondences with the biomechanical behavior estimation and allows the networks to tackle the full non-rigid registration problem in one single step. The result is a model which can quickly predict the volume deformation of a liver, given only sparse surface information. The model combines the advantages of a physically accurate biomechanical simulation with the speed and powerful feature extraction capabilities of deep neural networks. To test the method intraoperatively, a registration pipeline is developed which constructs a map of the liver and its surroundings from the laparoscopic video and then uses the neural networks to fuse the preoperative volume data into this map. The deformed organ volume can then be rendered as an overlay directly onto the laparoscopic video stream. The focus of this pipeline is to be applicable to real surgery, where everything should be quick and non-intrusive. To meet these requirements, a SLAM system is used to localize the laparoscopic camera (avoiding setup of an external tracking system), various neural networks are used to quickly interpret the scene and semi-automatic tools let the surgeons guide the system. Beyond the concrete advantages of the data-driven approach for intraoperative registration, this work also demonstrates general benefits of training a registration system preoperatively on synthetic data. The method lets the engineer decide which values need to be known explicitly and which should be estimated implicitly by the networks, which opens the door to many new possibilities.:1 Introduction 1.1 Motivation 1.1.1 Navigated Liver Surgery 1.1.2 Laparoscopic Liver Registration 1.2 Challenges in Laparoscopic Liver Registration 1.2.1 Preoperative Model 1.2.2 Intraoperative Data 1.2.3 Fusion/Registration 1.2.4 Data 1.3 Scope and Goals of this Work 1.3.1 Data-Driven, Biomechanical Model 1.3.2 Data-Driven Non-Rigid Registration 1.3.3 Building a Working Prototype 2 State of the Art 2.1 Rigid Registration 2.2 Non-Rigid Liver Registration 2.3 Neural Networks for Simulation and Registration 3 Theoretical Background 3.1 Liver 3.2 Laparoscopic Liver Resection 3.2.1 Staging Procedure 3.3 Biomechanical Simulation 3.3.1 Physical Balance Principles 3.3.2 Material Models 3.3.3 Numerical Solver: The Finite Element Method (FEM) 3.3.4 The Lagrangian Specification 3.4 Variables and Data in Liver Registration 3.4.1 Observable 3.4.2 Unknowns 4 Generating Simulations of Deforming Organs 4.1 Organ Volume 4.2 Forces and Boundary Conditions 4.2.1 Surface Forces 4.2.2 Zero-Displacement Boundary Conditions 4.2.3 Surrounding Tissues and Ligaments 4.2.4 Gravity 4.2.5 Pressure 4.3 Simulation 4.3.1 Static Simulation 4.3.2 Dynamic Simulation 4.4 Surface Extraction 4.4.1 Partial Surface Extraction 4.4.2 Surface Noise 4.4.3 Partial Surface Displacement 4.5 Voxelization 4.5.1 Voxelizing the Liver Geometry 4.5.2 Voxelizing the Displacement Field 4.5.3 Voxelizing Boundary Conditions 4.6 Pruning Dataset - Removing Unwanted Results 4.7 Data Augmentation 5 Deep Neural Networks for Biomechanical Simulation 5.1 Training Data 5.2 Network Architecture 5.3 Loss Functions and Training 6 Deep Neural Networks for Non-Rigid Registration 6.1 Training Data 6.2 Architecture 6.3 Loss 6.4 Training 6.5 Mesh Deformation 6.6 Example Application 7 Intraoperative Prototype 7.1 Image Acquisition 7.2 Stereo Calibration 7.3 Image Rectification, Disparity- and Depth- estimation 7.4 Liver Segmentation 7.4.1 Synthetic Image Generation 7.4.2 Automatic Segmentation 7.4.3 Manual Segmentation Modifier 7.5 SLAM 7.6 Dense Reconstruction 7.7 Rigid Registration 7.8 Non-Rigid Registration 7.9 Rendering 7.10 Robotic Operating System 8 Evaluation 8.1 Evaluation Datasets 8.1.1 In-Silico 8.1.2 Phantom Torso and Liver 8.1.3 In-Vivo, Human, Breathing Motion 8.1.4 In-Vivo, Human, Laparoscopy 8.2 Metrics 8.2.1 Mean Displacement Error 8.2.2 Target Registration Error (TRE) 8.2.3 Champfer Distance 8.2.4 Volumetric Change 8.3 Evaluation of the Synthetic Training Data 8.4 Data-Driven Biomechanical Model (DDBM) 8.4.1 Amount of Intraoperative Surface 8.4.2 Dynamic Simulation 8.5 Volume to Surface Registration Network (V2S-Net) 8.5.1 Amount of Intraoperative Surface 8.5.2 Dependency on Initial Rigid Alignment 8.5.3 Registration Accuracy in Comparison to Surface Noise 8.5.4 Registration Accuracy in Comparison to Material Stiffness 8.5.5 Champfer-Distance vs. Mean Displacement Error 8.5.6 In-vivo, Human Breathing Motion 8.6 Full Intraoperative Pipeline 8.6.1 Intraoperative Reconstruction: SLAM and Intraoperative Map 8.6.2 Full Pipeline on Laparoscopic Human Data 8.7 Timing 9 Discussion 9.1 Intraoperative Model 9.2 Physical Accuracy 9.3 Limitations in Training Data 9.4 Limitations Caused by Difference in Pre- and Intraoperative Modalities 9.5 Ambiguity 9.6 Intraoperative Prototype 10 Conclusion 11 List of Publications List of Figures Bibliograph

Bildbasierte Weichgeweberegistrierung in der Laparoskopie

Die minimal-invasive Chirurgie bietet viele Vorteile für den Patienten. Durch die Reduzierung des Operationstraumas und der damit beschleunigten Rekonvaleszenz des Patienten können zudem die Zeit der stationären Behandlung und damit auch die Kosten für das Gesundheitssystem reduziert werden. Dem gegenüber steht die höhere Belastung der Chirurgen während der Operation. Erst nach jahrelangem Training sind Ärzte in der Lage, die Herausforderungen dieser speziellen Operationstechnik zu meistern. Um Chirurgen bei dieser schwierigen Aufgabe zu unterstützen, wurden in den letzten Jahren durch die Verfügbarkeit von neuen Technologien verstärkt computergestützte Assistenzsysteme entwickelt. Während beispielsweise in der Neurochirurgie schon kommerzielle Assistenzsysteme existieren, gibt es in der Laparoskopie ein großes ungelöstes Problem: die Weichgeweberegistrierung. Um die detaillierten Organmodelle aus präoperativen Planungsdaten (bspw. aus der Computertomografie) während der Operation nutzen zu können, müssen diese an die Position, Ausrichtung und Form des intraoperativen Organs angeglichen werden. Diese nicht-rigide Anpassung des Modells wird als Weichgeweberegistrierung bezeichnet. Dabei werden die Verschiebungen und Deformationen der Organe des Patienten sowohl durch zuvor verursache Änderungen, wie der Lagerung des Patienten oder dem Anlegen des Pneumoperitoneums (Füllen und Aufblähen des Bauchraums mit CO$_2$-Gas), als auch durch dynamische Ereignisse während der Operation, wie der Atmung des Patienten oder Manipulationen der chirurgischen Instrumente, verursacht. Im Rahmen dieser Arbeit wurden die verschiedenen Bestandteile und Schritte für die laparoskopischen Weichgeweberegistrierung untersucht. Zur Erzeugung von intraoperativen 3D-Modellen wurde ein auf Convolutional Neuronalen Netzen basiertes Stereorekonstruktionsverfahren entwickelt, welches Disparitäten endoskopischer Bilddaten durch das Training mit domänenspezifischen Trainingsdaten bestimmt. Da für endoskopische Bilddaten nur sehr schwer eine Referenz für die Tiefendaten bestimmt werden kann, wurde ein mehrstufiger Trainingsprozess entwickelt. Aufgrund der speziellen Endoskop-Optik und den Eigenheiten dieser Bildgebung, bspw. Glanzlichter und texturarme, kantenfreie Oberflächen, sind endoskopische Trainingsdaten jedoch unverzichtbar, um bestmögliche Resultate zu erzielen. Hierzu wurden einerseits virtuelle Stereobilder von endoskopischen Simulationen erzeugt, andererseits wurden vorhandene reale Aufnahmen genutzt, um daraus durch die Erkennung von Landmarken, vollautomatisch dünnbesetzte Referenzkarten zu erzeugen. Das Verfahren wurde mit einem öffentlichen Datensatz evaluiert und konnte eine hohe Genauigkeit bei geringer Laufzeit demonstrieren. Für den eigentlichen Registrierungsprozess wurde ein zweistufiges Verfahren entwickelt. Im ersten Schritt wird zu Beginn der Operation eine initiale Weichgeweberegistrierung durchgeführt. Da die Verschiebungen, Rotationen und Deformationen zwischen präoperativer Aufnahme und Operation sehr groß sein können, ist hier ein möglichst umfangreiches intraoperatives Modell des betrachteten Organs wünschenswert. Mit dem in dieser Arbeit entwickelten Mosaikverfahren kann ein globales Oberflächenmodell aus mehreren Rekonstruktionsfragmenten der einzelnen Aufnahmen erzeugt werden. Die Evaluation zeigt eine starke Verringerung des Registrierungsfehlers, im Vergleich zur Nutzung von einzelnen Oberflächenfragmenten. Um dynamische Deformationen während der Operation auf das präoperative Modell zu übertragen, wurde ein Verfahren zur dynamischen Registrierung entwickelt. Dabei werden die präoperativen Daten durch ein biomechanisches Modell repräsentiert. Dieses Modell wird durch die Projektion in das aktuelle Kamerabild mit den Punkten der intraoperativen 3D-Rekonstruktion verknüpft. Diese Verknüpfungen dienen anschließend als Randbedingungen für eine FEM-Simulation, die das biomechanische Modell in jedem Zeitschritt an das intraoperative Organ anpasst. In einer in silico Evaluation und einem ersten Tierversuch konnte das Verfahren vielversprechende Ergebnisse vorweisen. Neben den eigentlichen Verfahren zur Weichgeweberegistrierung ist auch deren Evaluation von Bedeutung. Hier zeigt sich, dass künstliche Organmodelle ein wichtiges Bindeglied zwischen Simulationen und Tierversuchen darstellen. Für die Evaluation von Registrierungsalgorithmen sind vor allem die mechanischen Eigenschaften des Organmodells von Bedeutung. Der Guss von Silikonorganen ist einfach und kostengünstig, hat aufgrund des verwendeten Silikons allerdings den Nachteil, dass die Modelle deutlich härter als vergleichbares Weichgewebe sind. Um ein weiches Organmodell zu erstellen und gleichzeitig die Vorteile des Silikongusses beizubehalten, wurde in dieser Arbeit ein spezielles 3D-Druckverfahren erforscht. Dabei wird ein Negativgussmodell des Organs aus wasserlöslichem Material mit einem 3D-Drucker hergestellt. Die Besonderheit ist eine Gitterstruktur, die sich durch das ganze Gussmodell zieht. Nach dem Einfüllen und Aushärten des Silikons kann die Gussform mitsamt der innen liegenden Gitterstruktur aufgelöst werden. Dadurch entstehen überall im Silikonmodell kleine Hohlräume, welche die Struktur des Modells schwächen. In dem die Gitterstruktur vor dem Druckprozess angepasst wird kann der Härtegrad des späteren Modells in einem Rahmen von 30-100% des Silikon-Vollmodells eingestellt werden. Mechanische Experimente konnten die zuvor in der Simulation berechneten Kennwerte bestätigen

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

Electromagnetic tracking in image‐guided laparoscopic surgery: Comparison with optical tracking and feasibility study of a combined laparoscope and laparoscopic ultrasound system

PURPOSE: In image‐guided laparoscopy, optical tracking is commonly employed, but electromagnetic (EM) systems have been proposed in the literature. In this paper, we provide a thorough comparison of EM and optical tracking systems for use in image‐guided laparoscopic surgery and a feasibility study of a combined, EM‐tracked laparoscope and laparoscopic ultrasound (LUS) image guidance system. METHODS: We first assess the tracking accuracy of a laparoscope with two optical trackers tracking retroreflective markers mounted on the shaft and an EM tracker with the sensor embedded at the proximal end, using a standard evaluation plate. We then use a stylus to test the precision of position measurement and accuracy of distance measurement of the trackers. Finally, we assess the accuracy of an image guidance system comprised of an EM‐tracked laparoscope and an EM‐tracked LUS probe. RESULTS: In the experiment using a standard evaluation plate, the two optical trackers show less jitter in position and orientation measurement than the EM tracker. Also, the optical trackers demonstrate better consistency of orientation measurement within the test volume. However, their accuracy of measuring relative positions decreases significantly with longer distances whereas the EM tracker's performance is stable; at 50 mm distance, the RMS errors for the two optical trackers are 0.210 and 0.233 mm, respectively, and it is 0.214 mm for the EM tracker; at 250 mm distance, the RMS errors for the two optical trackers become 1.031 and 1.178 mm, respectively, while it is 0.367 mm for the EM tracker. In the experiment using the stylus, the two optical trackers have RMS errors of 1.278 and 1.555 mm in localizing the stylus tip, and it is 1.117 mm for the EM tracker. Our prototype of a combined, EM‐tracked laparoscope and LUS system using representative calibration methods showed a RMS point localization error of 3.0 mm for the laparoscope and 1.3 mm for the LUS probe, the lager error of the former being predominantly due to the triangulation error when using a narrow‐baseline stereo laparoscope. CONCLUSIONS: The errors incurred by optical trackers, due to the lever‐arm effect and variation in tracking accuracy in the depth direction, would make EM‐tracked solutions preferable if the EM sensor is placed at the proximal end of the laparoscope

Image-Based Scene Analysis for Computer-Assisted Laparoscopic Surgery

This thesis is concerned on image-based scene analysis for computer-assisted laparoscopic surgery. The focus lies on how to extract different types of information from laparoscopic video data. Methods for semantic analysis can be used to determine what instruments and organs are currently visible and where they are located. Quantitative analysis provides numerical information on the size and distances of structures. Workflow analysis uses information from previously seen images to estimate the progression of surgery. To demonstrate that the proposed methods function in real-world scenarios, multiple evaluations on actual laparoscopic image data recorded from surgeries were performed. The proposed methods for semantic and quantitative analysis were successfully evaluated in live phantom and animal studies and also used during a live gastric bypass on a human patient

Development of an image guidance system for laparoscopic liver surgery and evaluation of optical and computer vision techniques for the assessment of liver tissue

Introduction: Liver resection is increasingly being carried out via the laparoscopic approach (keyhole surgery) because there is mounting evidence that it benefits patients by reducing pain and length of hospitalisation. There are however ongoing concerns about oncological radicality (i.e. ability to completely remove cancer) and an inability to control massive haemorrhage. These issues can partially be attributed to a loss of sensation such as depth perception, tactile feedback and a reduced field of view. Utilisation of optical imaging and computer vision may be able to compensate for some of the lost sensory input because these modalities can facilitate visualisation of liver tissue and structural anatomy. Their use in laparoscopy is attractive because it is easy to adapt or integrate with existing technology. The aim of this thesis is to explore to what extent this technology can aid in the detection of normal and abnormal liver tissue and structures. / Methods: The current state of the art for optical imaging and computer vision in laparoscopic liver surgery is assessed in a systematic review. Evaluation of confocal laser endomicroscopy is carried out on a murine and porcine model of liver disease. Multispectral near infrared imaging is evaluated on ex-vivo liver specimen. Video magnification is assessed on a mechanical flow phantom and a porcine model of liver disease. The latter model was also employed to develop a computer vision based image guidance system for laparoscopic liver surgery. This image guidance system is further evaluated in a clinical feasibility study. Where appropriate, experimental findings are substantiated with statistical analysis. / Results: Use of confocal laser endomicroscopy enabled discrimination between cancer and normal liver tissue with a sub-millimetre precision. This technology also made it possible to verify the adequacy of thermal liver ablation. Multispectral imaging, at specific wavelengths was shown to have the potential to highlight the presence of colorectal and hepatocellular cancer. An image reprocessing algorithm is proposed to simplify visual interpretation of the resulting images. It is shown that video magnification can determine the presence of pulsatile motion but that it cannot reliably determine the extent of motion. Development and performance metrics of an image guidance system for laparoscopic liver surgery are outlined. The system was found to improve intraoperative orientation more development work is however required to enable reliable prediction of oncological margins. / Discussion: The results in this thesis indicate that confocal laser endomicroscopy and image guidance systems have reached a development stage where their intraoperative use may benefit surgeons by visualising features of liver anatomy and tissue characteristics. Video magnification and multispectral imaging require more development and suggestions are made to direct this work. It is also highlighted that it is crucial to standardise assessment methods for these technologies which will allow a more direct comparison between the outcomes of different groups. Limited imaging depth is a major restriction of these technologies but this may be overcome by combining them with preoperatively obtained imaging data. Just like laparoscopy, optical imaging and computer vision use functions of light, a shared characteristic that makes their combined use complementary

Scene Reconstruction Beyond Structure-from-Motion and Multi-View Stereo

Image-based 3D reconstruction has become a robust technology for recovering accurate and realistic models of real-world objects and scenes. A common pipeline for 3D reconstruction is to first apply Structure-from-Motion (SfM), which recovers relative poses for the input images and sparse geometry for the scene, and then apply Multi-view Stereo (MVS), which estimates a dense depthmap for each image. While this two-stage process is quite effective in many 3D modeling scenarios, there are limits to what can be reconstructed. This dissertation focuses on three particular scenarios where the SfM+MVS pipeline fails and introduces new approaches to accomplish each reconstruction task. First, I introduce a novel method to recover dense surface reconstructions of endoscopic video. In this setting, SfM can generally provide sparse surface structure, but the lack of surface texture as well as complex, changing illumination often causes MVS to fail. To overcome these difficulties, I introduce a method that utilizes SfM both to guide surface reflectance estimation and to regularize shading-based depth reconstruction. I also introduce models of reflectance and illumination that improve the final result. Second, I introduce an approach for augmenting 3D reconstructions from large-scale Internet photo-collections by recovering the 3D position of transient objects --- specifically, people --- in the input imagery. Since no two images can be assumed to capture the same person in the same location, the typical triangulation constraints enjoyed by SfM and MVS cannot be directly applied. I introduce an alternative method to approximately triangulate people who stood in similar locations, aided by a height distribution prior and visibility constraints provided by SfM. The scale of the scene, gravity direction, and per-person ground-surface normals are also recovered. Finally, I introduce the concept of using crowd-sourced imagery to create living 3D reconstructions --- visualizations of real places that include dynamic representations of transient objects. A key difficulty here is that SfM+MVS pipelines often poorly reconstruct ground surfaces given Internet images. To address this, I introduce a volumetric reconstruction approach that leverages scene scale and person placements. Crowd simulation is then employed to add virtual pedestrians to the space and bring the reconstruction "to life."Doctor of Philosoph

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered