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

Robotic 3D Reconstruction Utilising Structure from Motion

Sensing the real-world is a well-established and continual problem in the field of robotics. Investigations into autonomous aerial and underwater vehicles have extended this challenge into sensing, mapping and localising in three dimensions. This thesis seeks to understand and tackle the challenges of recovering 3D information from an environment using vision alone. There is a well-established literature on the principles of doing this, and some impressive demonstrations; but this thesis explores the practicality of doing vision-based 3D reconstruction using multiple, mobile robotic platforms, the emphasis being on producing accurate 3D models. Typically, robotic platforms such as UAVs have a single on-board camera, restricting which method of visual 3D recovery can be employed. This thesis specifically explores Structure from Motion, a monocular 3D reconstruction technique which produces detailed and accurate, although slow to calculate, 3D reconstructions. It examines how well proof-of-concept demonstrations translate onto the kinds of robotic systems that are commonly deployed in the real world, where local processing is limited and network links have restricted capacity. In order to produce accurate 3D models, it is necessary to use high-resolution imagery, and the difficulties of working with this on remote robotic platforms is explored in some detail

Vegetation detection and terrain classification for autonomous navigation

Diese Arbeit beleuchtet sieben neuartige Ansätze aus zwei Bereichen der maschinellen Wahrnehmung: Erkennung von Vegetation und Klassifizierung von Gelände. Diese Elemente bilden den Kern eines jeden Steuerungssystems für effiziente, autonome Navigation im Außenbereich. Bezüglich der Vegetationserkennung, wird zuerst ein auf Indizierung basierender Ansatz beschrieben (1), der die reflektierenden und absorbierenden Eigenschaften von Pflanzen im Bezug auf sichtbares und nah-infrarotes Licht auswertet. Zweitens wird eine Fusionmethode von 2D/3D Merkmalen untersucht (2), die das menschliche System der Vegetationserkennung nachbildet. Zusätzlich wird ein integriertes System vorgeschlagen (3), welches die visuelle Wahrnehmung mit multi-spektralen Methoden ko mbiniert. Aufbauend auf detaillierten Studien zu Farb- und Textureigenschaften von Vegetation wird ein adaptiver selbstlernender Algorithmus eingeführt der robust und schnell Pflanzen(bewuchs) erkennt (4). Komplettiert wird die Vegetationserkennung durch einen Algorithmus zur Befahrbarkeitseinschätzung von Vegetation, der die Verformbarkeit von Pflanzen erkennt. Je leichter sich Pflanzen bewegen lassen, umso größer ist ihre Befahrbarkeit. Bezüglich der Geländeklassifizierung wird eine struktur-basierte Methode vorgestellt (6), welche die 3D Strukturdaten einer Umgebung durch die statistische Analyse lokaler Punkte von LiDAR Daten unterstützt. Zuletzt wird eine auf Klassifizierung basierende Methode (7) beschrieben, die LiDAR und Kamera-Daten kombiniert, um eine 3D Szene zu rekonstruieren. Basierend auf den Vorteilen der vorgestellten Algorithmen im Bezug auf die maschinelle Wahrnehmung, hoffen wir, dass diese Arbeit als Ausgangspunkt für weitere Entwicklung en von zuverlässigen Erkennungsmethoden dient.This thesis introduces seven novel contributions for two perception tasks: vegetation detection and terrain classification, that are at the core of any control system for efficient autonomous navigation in outdoor environments. Regarding vegetation detection, we first describe a vegetation index-based method (1), which relies on the absorption and reflectance properties of vegetation to visual light and near-infrared light, respectively. Second, a 2D/3D feature fusion (2), which imitates the human visual system in vegetation interpretation, is investigated. Alternatively, an integrated vision system (3) is proposed to realise our greedy ambition in combining visual perception-based and multi-spectral methods by only using a unit device. A depth study on colour and texture features of vegetation has been carried out, which leads to a robust and fast vegetation detection through an adaptive learning algorithm (4). In addition, a double-check of passable vegetation detection (5) is realised, relying on the compressibility of vegetation. The lower degree of resistance vegetation has, the more traversable it is. Regarding terrain classification, we introduce a structure-based method (6) to capture the world scene by inferring its 3D structures through a local point statistic analysis on LiDAR data. Finally, a classification-based method (7), which combines the LiDAR data and visual information to reconstruct 3D scenes, is presented. Whereby, object representation is described more details, thus enabling an ability to classify more object types. Based on the success of the proposed perceptual inference methods in the environmental sensing tasks, we hope that this thesis will really serve as a key point for further development of highly reliable perceptual inference methods

On Semantic Segmentation and Path Planning for Autonomous Vehicles within Off-Road Environments

There are many challenges involved in creating a fully autonomous vehicle capable of safely navigating through off-road environments. In this work we focus on two of the most prominent such challenges, namely scene understanding and path planning. Scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we build on recent work in urban road-scene understanding, training a state of the art CNN architecture towards the task of classifying off-road scenes. We analyse the effects of transfer learning and training data set size on CNN performance, evaluating multiple configurations of the network at multiple points during the training cycle, investigating in depth how the training process is affected. We compare this CNN to a more traditional feature-driven approach with Support Vector Machine (SVM) classifier and demonstrate state-of-the-art results in this particularly challenging problem of off-road scene understanding. We then expand on this with the addition of multi-channel RGBD data, which we encode in multiple configurations for CNN input. We evaluate each of these configuration over our own off-road RGBD data set and compare performance to that of the network model trained using RGB data. Next, we investigate end-to-end navigation, whereby a machine learning algorithm optimises to predict the vehicle control inputs of a human driver. After evaluating such a technique in an off-road environment and identifying several limitations, we propose a new approach in which a CNN learns to predict vehicle path visually, combining a novel approach to automatic training data creation with state of the art CNN architecture to map a predicted route directly onto image pixels. We then evaluate this approach using our off-road data set, and demonstrate effectiveness surpassing existing end-to-end methods

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

Exploration and Implementation of Augmented Reality for External Beam Radiotherapy

We have explored applications of Augmented Reality (AR) for external beam radiotherapy to assist with treatment planning, patient education, and treatment delivery. We created an AR development framework for applications in radiotherapy (RADiotherapy Augmented Reality, RAD-AR) for AR ready consumer electronics such as tablet computers and head mounted devices (HMD). We implemented in RAD-AR three tools to assist radiotherapy practitioners with: treatment plans evaluation, patient pre-treatment information/education, and treatment delivery. We estimated accuracy and precision of the patient setup tool and the underlying self-tracking technology, and fidelity of AR content geometric representation, on the Apple iPad tablet computer and the Microsoft HoloLens HMD. Results showed that the technology could already be applied for detection of large treatment setup errors, and could become applicable to other aspects of treatment delivery subject to technological improvements that can be expected in the near future. We performed user feedback studies of the patient education and the plan evaluation tools. Results indicated an overall positive user evaluation of AR technology compared to conventional tools for the radiotherapy elements implemented. We conclude that AR will become a useful tool in radiotherapy bringing real benefits for both clinicians and patients, contributing to successful treatment outcomes

Fusion of LIDAR with stereo camera data - an assessment

This thesis explores data fusion of LIDAR (laser range-finding) with stereo matching, with a particular emphasis on close-range industrial 3D imaging. Recently there has been interest in improving the robustness of stereo matching using data fusion with active range data. These range data have typically been acquired using time of flight cameras (ToFCs), however ToFCs offer poor spatial resolution and are noisy. Comparatively little work has been performed using LIDAR. It is argued that stereo and LIDAR are complementary and there are numerous advantages to integrating LIDAR into stereo systems. For instance, camera calibration is a necessary prerequisite for stereo 3D reconstruction, but the process is often tedious and requires precise calibration targets. It is shown that a visible-beam LIDAR enables automatic, accurate (sub-pixel) extrinsic and intrinsic camera calibration without any explicit targets. Two methods for using LIDAR to assist dense disparity maps from featureless scenes were investigated. The first involved using a LIDAR to provide high-confidence seed points for a region growing stereo matching algorithm. It is shown that these seed points allow dense matching in scenes which fail to match using stereo alone. Secondly, LIDAR was used to provide artificial texture in featureless image regions. Texture was generated by combining real or simulated images of every point the laser hits to form a pseudo-random pattern. Machine learning was used to determine the image regions that are most likely to be stereo- matched, reducing the number of LIDAR points required. Results are compared to competing techniques such as laser speckle, data projection and diffractive optical elements

The selection and evaluation of a sensory technology for interaction in a warehouse environment

In recent years, Human-Computer Interaction (HCI) has become a significant part of modern life as it has improved human performance in the completion of daily tasks in using computerised systems. The increase in the variety of bio-sensing and wearable technologies on the market has propelled designers towards designing more efficient, effective and fully natural User-Interfaces (UI), such as the Brain-Computer Interface (BCI) and the Muscle-Computer Interface (MCI). BCI and MCI have been used for various purposes, such as controlling wheelchairs, piloting drones, providing alphanumeric inputs into a system and improving sports performance. Various challenges are experienced by workers in a warehouse environment. Because they often have to carry objects (referred to as hands-full) it is difficult to interact with traditional devices. Noise undeniably exists in some industrial environments and it is known as a major factor that causes communication problems. This has reduced the popularity of using verbal interfaces with computer applications, such as Warehouse Management Systems. Another factor that effects the performance of workers are action slips caused by a lack of concentration during, for example, routine picking activities. This can have a negative impact on job performance and allow a worker to incorrectly execute a task in a warehouse environment. This research project investigated the current challenges workers experience in a warehouse environment and the technologies utilised in this environment. The latest automation and identification systems and technologies are identified and discussed, specifically the technologies which have addressed known problems. Sensory technologies were identified that enable interaction between a human and a computerised warehouse environment. Biological and natural behaviours of humans which are applicable in the interaction with a computerised environment were described and discussed. The interactive behaviours included the visionary, auditory, speech production and physiological movement where other natural human behaviours such paying attention, action slips and the action of counting items were investigated. A number of modern sensory technologies, devices and techniques for HCI were identified with the aim of selecting and evaluating an appropriate sensory technology for MCI. iii MCI technologies enable a computer system to recognise hand and other gestures of a user, creating means of direct interaction between a user and a computer as they are able to detect specific features extracted from a specific biological or physiological activity. Thereafter, Machine Learning (ML) is applied in order to train a computer system to detect these features and convert them to a computer interface. An application of biomedical signals (bio-signals) in HCI using a MYO Armband for MCI is presented. An MCI prototype (MCIp) was developed and implemented to allow a user to provide input to an HCI, in a hands-free and hands-full situation. The MCIp was designed and developed to recognise the hand-finger gestures of a person when both hands are free or when holding an object, such a cardboard box. The MCIp applies an Artificial Neural Network (ANN) to classify features extracted from the surface Electromyography signals acquired by the MYO Armband around the forearm muscle. The MCIp provided the results of data classification for gesture recognition to an accuracy level of 34.87% with a hands-free situation. This was done by employing the ANN. The MCIp, furthermore, enabled users to provide numeric inputs to the MCIp system hands-full with an accuracy of 59.7% after a training session for each gesture of only 10 seconds. The results were obtained using eight participants. Similar experimentation with the MYO Armband has not been found to be reported in any literature at submission of this document. Based on this novel experimentation, the main contribution of this research study is a suggestion that the application of a MYO Armband, as a commercially available muscle-sensing device on the market, has the potential as an MCI to recognise the finger gestures hands-free and hands-full. An accurate MCI can increase the efficiency and effectiveness of an HCI tool when it is applied to different applications in a warehouse where noise and hands-full activities pose a challenge. Future work to improve its accuracy is proposed