Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review

Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications

Smart Surgical Microscope based on Optical Coherence Domain Reflectometry

Department of Biomedical EngineeringOver the several decades, there have been clinical needs that requires advanced technologies in medicine. Optical coherence tomography (OCT), one of the newly emerged medical imaging devices, provides non-invasive cross-sectional images in high resolution which is mainly used in ophthalmology. However, due to the limited penetration depth of 1-2 mm in bio-samples, there is a limit to be widely used. In order to easily integrate with existing medical tools and be convenient to users, it is necessary that the sample unit of OCT should be compact and simple. In this study, we developed high-speed swept-source OCT (SS-OCT) for advanced screening of otolaryngology. Synchronized signal sampling with a high-speed digitizer using a clock signal from a swept laser source, its trigger signal is also used to synchronize with the movement of the scanning mirror. The SS-OCT system can reliably provide high-throughput images, and two-axis scanning of galvano mirrors enables real-time acquisition of 3D data. Graphic processing unit (GPU) can performs high-speed data processing through parallel programming, and can also implement perspective projection 3D OCT visualization with optimal ray casting techniques. In the Clinical Study of Otolaryngology, OCT was applied to identify the microscopic extrathyroidal extension (mETE) of papillary thyroid cancer (PTC). As a result to detect the mETE of around 60% in conventional ultrasonography, it could be improved to 84.1% accuracy in our study. The detection ratio of the mETE was calculated by the pathologist analyzing the histologic image. In chapter 3, we present a novel study using combined OCT system integrated with a conventional surgical microscope. In the current set-up of surgical microscope, only two-dimensional microscopic images through the eyepiece view are provided to the surgeon. Thus, image-guided surgery, which provides real-time image information of the tissues or the organs, has been developed as an advanced surgical technique. This study illustrate newly designed optical set-up of smart surgical microscope that combined sample arm of the OCT with an existing microscope. Specifically, we used a beam projector to overlay OCT images on existing eyepiece views, and demonstrated augmented reality images. In chapter 4, in order to develop novel microsurgical instruments, optical coherence domain reflectometry (OCDR) was applied. Introduces smart surgical forceps using OCDR as a sensor that provides high-speed, high-resolution distance information in the tissue. To attach the sensor to the forceps, the lensed fiber which is a small and high sensitivity sensor was fabricated and the results are shown to be less affected by the tilt angle. In addition, the piezo actuator compensates the hand tremor, resulting in a reduction in the human hand tremor of 5 to 15 Hz. Finally, M-mode OCT needle is proposed for microsurgery guidance in ophthalmic surgery. Stepwise transitional core (STC) fiber was applied as a sensor to measure information within the tissue and attached to a 26 gauge needle. It shows the modified OCT system and the position-guided needle design of the sample stage and shows the algorithm flowchart of M-mode OCT imaging software. The developed M-mode OCT needle has been applied to animal studies using rabbit eyes and demonstrates the big-bubble deep anterior lamellar keratoplasty (DALK) surgery for corneal transplantation. Through this study, we propose a novel microsurgical instrument for lamellar keratoplasty and evaluate its feasibility with conventional regular OCT system images. In conclusion, for fundamental study required new augmented reality guided surgery with smart surgical microscope, it is expected that OCT combined with surgical microscope can be widely used. We demonstrated a novel microsurgical instrument to share with light source and the various optical components. Acquired information throughout our integrated system would be a key method to meet a wide range of different clinical needs in the real world.ope

Optimización en GPU de algoritmos para la mejora del realce y segmentación en imágenes hepáticas

This doctoral thesis deepens the GPU acceleration for liver enhancement and segmentation. With this motivation, detailed research is carried out here in a compendium of articles. The work developed is structured in three scientific contributions, the first one is based upon enhancement and tumor segmentation, the second one explores the vessel segmentation and the last is published on liver segmentation. These works are implemented on GPU with significant speedups with great scientific impact and relevance in this doctoral thesis The first work proposes cross-modality based contrast enhancement for tumor segmentation on GPU. To do this, it takes target and guidance images as an input and enhance the low quality target image by applying two dimensional histogram approach. Further it has been observed that the enhanced image provides more accurate tumor segmentation using GPU based dynamic seeded region growing. The second contribution is about fast parallel gradient based seeded region growing where static approach has been proposed and implemented on GPU for accurate vessel segmentation. The third contribution describes GPU acceleration of Chan-Vese model and cross-modality based contrast enhancement for liver segmentation

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

Patient-specific simulation for autonomous surgery

An Autonomous Robotic Surgical System (ARSS) has to interact with the complex anatomical environment, which is deforming and whose properties are often uncertain. Within this context, an ARSS can benefit from the availability of patient-specific simulation of the anatomy. For example, simulation can provide a safe and controlled environment for the design, test and validation of the autonomous capabilities. Moreover, it can be used to generate large amounts of patient-specific data that can be exploited to learn models and/or tasks. The aim of this Thesis is to investigate the different ways in which simulation can support an ARSS and to propose solutions to favor its employability in robotic surgery. We first address all the phases needed to create such a simulation, from model choice in the pre-operative phase based on the available knowledge to its intra-operative update to compensate for inaccurate parametrization. We propose to rely on deep neural networks trained with synthetic data both to generate a patient-specific model and to design a strategy to update model parametrization starting directly from intra-operative sensor data. Afterwards, we test how simulation can assist the ARSS, both for task learning and during task execution. We show that simulation can be used to efficiently train approaches that require multiple interactions with the environment, compensating for the riskiness to acquire data from real surgical robotic systems. Finally, we propose a modular framework for autonomous surgery that includes deliberative functions to handle real anatomical environments with uncertain parameters. The integration of a personalized simulation proves fundamental both for optimal task planning and to enhance and monitor real execution. The contributions presented in this Thesis have the potential to introduce significant step changes in the development and actual performance of autonomous robotic surgical systems, making them closer to applicability to real clinical conditions

Digital Image Processing Applications

Digital image processing can refer to a wide variety of techniques, concepts, and applications of different types of processing for different purposes. This book provides examples of digital image processing applications and presents recent research on processing concepts and techniques. Chapters cover such topics as image processing in medical physics, binarization, video processing, and more

Natural ventilation design attributes application effect on, indoor natural ventilation performance of a double storey, single unit residential building

In establishing a good indoor thermal condition, air movement is one of the important parameter to be considered to provide indoor fresh air for occupants. Due to the public awareness on environment impact, people has been increasingly attentive to passive design in achieving good condition of indoor building ventilation. Throughout case studies, significant building attributes were found giving effect on building indoor natural ventilation performance. The studies were categorized under vernacular houses, contemporary houses with vernacular element and contemporary houses. The indoor air movement of every each spaces in the houses were compared with the outdoor air movement surrounding the houses to indicate the space’s indoor natural ventilation performance. Analysis found the wind catcher element appears to be the most significant attribute to contribute most to indoor natural ventilation. Wide opening was also found to be significant especially those with louvers. Whereas it is also interesting to find indoor layout design is also significantly giving impact on the performance. The finding indicates that a good indoor natural ventilation is not only dictated by having proper openings at proper location of a building, but also on how the incoming air movement is managed throughout the interior spaces by proper layout. Understanding on the air pressure distribution caused by indoor windward and leeward side is important in directing the air flow to desired spaces in producing an overall good indoor natural ventilation performance

Using advanced illumination techniques to enhance realism and perception of volume visualizations

Die Nutzung volumetrischer Daten ist in vergangenen Jahren immer häufiger geworden. Die Erzeugung von aussagekräfigen und verständlichen Bildern aus diesen Daten ist daher wichtiger denn je. Die Simulation von Beleuchtungsphänomenen ist eine Möglichkeit, die Wahrnehmung und den Realismus solcher Bilder zu verbessern. Diese Dissertation beschäftigt sich mit der Effektivität von existierenden Modellen zur Volumenillumination und präsentiert einige neue Techniken und Anwendungen für diesen Bereich der Computergrafik. Es werden Methoden vorgestellt, um die Interaktion von Licht und Material im Kontext von Volumendaten zu simulieren. Weiterhin wird eine umfangreichenNutzerstudie präsentiert, deren Ziel es war, den Einfluss von verschiedenen existierenden Modellen zur Volumenillumination auf den Betrachter zu untersuchen. Abschließend wird eine Anwendung zur Darstellung und visuellen Analyse von Hirndaten präsentiert, in der Volumenillumination neben weiteren neuartigen Visualisierungen zum Einsatz kommt.<br

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519