Augmented Reality and Artificial Intelligence in Image-Guided and Robot-Assisted Interventions

In minimally invasive orthopedic procedures, the surgeon places wires, screws, and surgical implants through the muscles and bony structures under image guidance. These interventions require alignment of the pre- and intra-operative patient data, the intra-operative scanner, surgical instruments, and the patient. Suboptimal interaction with patient data and challenges in mastering 3D anatomy based on ill-posed 2D interventional images are essential concerns in image-guided therapies. State of the art approaches often support the surgeon by using external navigation systems or ill-conditioned image-based registration methods that both have certain drawbacks. Augmented reality (AR) has been introduced in the operating rooms in the last decade; however, in image-guided interventions, it has often only been considered as a visualization device improving traditional workflows. Consequently, the technology is gaining minimum maturity that it requires to redefine new procedures, user interfaces, and interactions. This dissertation investigates the applications of AR, artificial intelligence, and robotics in interventional medicine. Our solutions were applied in a broad spectrum of problems for various tasks, namely improving imaging and acquisition, image computing and analytics for registration and image understanding, and enhancing the interventional visualization. The benefits of these approaches were also discovered in robot-assisted interventions. We revealed how exemplary workflows are redefined via AR by taking full advantage of head-mounted displays when entirely co-registered with the imaging systems and the environment at all times. The proposed AR landscape is enabled by co-localizing the users and the imaging devices via the operating room environment and exploiting all involved frustums to move spatial information between different bodies. The system's awareness of the geometric and physical characteristics of X-ray imaging allows the exploration of different human-machine interfaces. We also leveraged the principles governing image formation and combined it with deep learning and RGBD sensing to fuse images and reconstruct interventional data. We hope that our holistic approaches towards improving the interface of surgery and enhancing the usability of interventional imaging, not only augments the surgeon's capabilities but also augments the surgical team's experience in carrying out an effective intervention with reduced complications

Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences

In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338

Sub-pixel Registration In Computational Imaging And Applications To Enhancement Of Maxillofacial Ct Data

In computational imaging, data acquired by sampling the same scene or object at different times or from different orientations result in images in different coordinate systems. Registration is a crucial step in order to be able to compare, integrate and fuse the data obtained from different measurements. Tomography is the method of imaging a single plane or slice of an object. A Computed Tomography (CT) scan, also known as a CAT scan (Computed Axial Tomography scan), is a Helical Tomography, which traditionally produces a 2D image of the structures in a thin section of the body. It uses X-ray, which is ionizing radiation. Although the actual dose is typically low, repeated scans should be limited. In dentistry, implant dentistry in specific, there is a need for 3D visualization of internal anatomy. The internal visualization is mainly based on CT scanning technologies. The most important technological advancement which dramatically enhanced the clinician\u27s ability to diagnose, treat, and plan dental implants has been the CT scan. Advanced 3D modeling and visualization techniques permit highly refined and accurate assessment of the CT scan data. However, in addition to imperfections of the instrument and the imaging process, it is not uncommon to encounter other unwanted artifacts in the form of bright regions, flares and erroneous pixels due to dental bridges, metal braces, etc. Currently, removing and cleaning up the data from acquisition backscattering imperfections and unwanted artifacts is performed manually, which is as good as the experience level of the technician. On the other hand the process is error prone, since the editing process needs to be performed image by image. We address some of these issues by proposing novel registration methods and using stonecast models of patient\u27s dental imprint as reference ground truth data. Stone-cast models were originally used by dentists to make complete or partial dentures. The CT scan of such stone-cast models can be used to automatically guide the cleaning of patients\u27 CT scans from defects or unwanted artifacts, and also as an automatic segmentation system for the outliers of the CT scan data without use of stone-cast models. Segmented data is subsequently used to clean the data from artifacts using a new proposed 3D inpainting approach

Medical Image Registration: Statistical Models of Performance in Relation to the Statistical Characteristics of the Image Data

For image-guided interventions, the imaging task often pertains to registering preoperative and intraoperative images within a common coordinate system. While the accuracy of the registration is directly tied to the accuracy of targeting in the intervention (and presumably the success of the medical outcome), there is relatively little quantitative understanding of the fundamental factors that govern image registration accuracy. A statistical framework is presented that relates models of image noise and spatial resolution to the task of registration, giving theoretical limits on registration accuracy and providing guidance for the selection of image acquisition and post-processing parameters. The framework is further shown to model the confounding influence of soft-tissue deformation in rigid image registration — accurately predicting the reduction in registration accuracy and revealing similarity metrics that are robust against such effects. Furthermore, the framework is shown to provide conceptual guidance in the development of a novel CT-to-radiograph registration method that accounts for deformation. The work also examines a learning-based method for deformable registration to investigate how the statistical characteristics of the training data affect the ability of the model to generalize to test data with differing statistical characteristics. The analysis provides insight on the benefits of statistically diverse training data in generalizability of a neural network and is further applied to the development of a learning-based MR-to-CT synthesis method. Overall, the work yields a quantitative approach to theoretically and experimentally relate the accuracy of image registration to the statistical characteristics of the image data, providing a rigorous guide to the development of new registration methods

An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images

We have developed an algorithm for the rigid-body registration of a CT volume to a set of C-arm images. The algorithm uses a gradient-based iterative minimization of a least-squares measure of dissimilarity between the C-arm images and projections of the CT volume. To compute projections, we use a novel method for fast integration of the volume along rays. To improve robustness and speed, we take advantage of a coarse-to-fine processing of the volume/image pyramids. To compute the projections of the volume, the gradient of the dissimilarity measure, and the multiresolution data pyramids, we use a continuous image/volume model based on cubic B-splines, which ensures a high interpolation accuracy and a gradient of the dissimilarity measure that is well defined everywhere. We show the performance of our algorithm on a human spine phantom, where the true alignment is determined using a set of fiducial markers

Exploiting Temporal Image Information in Minimally Invasive Surgery

Minimally invasive procedures rely on medical imaging instead of the surgeons direct vision. While preoperative images can be used for surgical planning and navigation, once the surgeon arrives at the target site real-time intraoperative imaging is needed. However, acquiring and interpreting these images can be challenging and much of the rich temporal information present in these images is not visible. The goal of this thesis is to improve image guidance for minimally invasive surgery in two main areas. First, by showing how high-quality ultrasound video can be obtained by integrating an ultrasound transducer directly into delivery devices for beating heart valve surgery. Secondly, by extracting hidden temporal information through video processing methods to help the surgeon localize important anatomical structures. Prototypes of delivery tools, with integrated ultrasound imaging, were developed for both transcatheter aortic valve implantation and mitral valve repair. These tools provided an on-site view that shows the tool-tissue interactions during valve repair. Additionally, augmented reality environments were used to add more anatomical context that aids in navigation and in interpreting the on-site video. Other procedures can be improved by extracting hidden temporal information from the intraoperative video. In ultrasound guided epidural injections, dural pulsation provides a cue in finding a clear trajectory to the epidural space. By processing the video using extended Kalman filtering, subtle pulsations were automatically detected and visualized in real-time. A statistical framework for analyzing periodicity was developed based on dynamic linear modelling. In addition to detecting dural pulsation in lumbar spine ultrasound, this approach was used to image tissue perfusion in natural video and generate ventilation maps from free-breathing magnetic resonance imaging. A second statistical method, based on spectral analysis of pixel intensity values, allowed blood flow to be detected directly from high-frequency B-mode ultrasound video. Finally, pulsatile cues in endoscopic video were enhanced through Eulerian video magnification to help localize critical vasculature. This approach shows particular promise in identifying the basilar artery in endoscopic third ventriculostomy and the prostatic artery in nerve-sparing prostatectomy. A real-time implementation was developed which processed full-resolution stereoscopic video on the da Vinci Surgical System

Segmentation and Fracture Detection in CT Images for Traumatic Pelvic Injuries

In recent decades, more types and quantities of medical data have been collected due to advanced technology. A large number of significant and critical information is contained in these medical data. High efficient and automated computational methods are urgently needed to process and analyze all available medical data in order to provide the physicians with recommendations and predictions on diagnostic decisions and treatment planning. Traumatic pelvic injury is a severe yet common injury in the United States, often caused by motor vehicle accidents or fall. Information contained in the pelvic Computed Tomography (CT) images is very important for assessing the severity and prognosis of traumatic pelvic injuries. Each pelvic CT scan includes a large number of slices. Meanwhile, each slice contains a large quantity of data that may not be thoroughly and accurately analyzed via simple visual inspection with the desired accuracy and speed. Hence, a computer-assisted pelvic trauma decision-making system is needed to assist physicians in making accurate diagnostic decisions and determining treatment planning in a short period of time. Pelvic bone segmentation is a vital step in analyzing pelvic CT images and assisting physicians with diagnostic decisions in traumatic pelvic injuries. In this study, a new hierarchical segmentation algorithm is proposed to automatically extract multiplelevel bone structures using a combination of anatomical knowledge and computational techniques. First, morphological operations, image enhancement, and edge detection are performed for preliminary bone segmentation. The proposed algorithm then uses a template-based best shape matching method that provides an entirely automated segmentation process. This is followed by the proposed Registered Active Shape Model (RASM) algorithm that extracts pelvic bone tissues using more robust training models than the Standard ASM algorithm. In addition, a novel hierarchical initialization process for RASM is proposed in order to address the shortcoming of the Standard ASM, i.e. high sensitivity to initialization. Two suitable measures are defined to evaluate the segmentation results: Mean Distance and Mis-segmented Area to quantify the segmentation accuracy. Successful segmentation results indicate effectiveness and robustness of the proposed algorithm. Comparison of segmentation performance is also conducted using both the proposed method and the Snake method. A cross-validation process is designed to demonstrate the effectiveness of the training models. 3D pelvic bone models are built after pelvic bone structures are segmented from consecutive 2D CT slices. Automatic and accurate detection of the fractures from segmented bones in traumatic pelvic injuries can help physicians detect the severity of injuries in patients. The extraction of fracture features (such as presence and location of fractures) as well as fracture displacement measurement, are vital for assisting physicians in making faster and more accurate decisions. In this project, after bone segmentation, fracture detection is performed using a hierarchical algorithm based on wavelet transformation, adaptive windowing, boundary tracing and masking. Also, a quantitative measure of fracture severity based on pelvic CT scans is defined and explored. The results are promising, demonstrating that the proposed method not only capable of automatically detecting both major and minor fractures, but also has potentials to be used for clinical applications

Medical Image Segmentation with Deep Convolutional Neural Networks

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and sparked research interests in medical image segmentation using deep learning. We propose three convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

Fusion and Analysis of Multidimensional Medical Image Data

Analýza medicínských obrazů je předmětem základního výzkumu již řadu let. Za tu dobu bylo v této oblasti publikováno mnoho výzkumných prací zabývajících se dílčími částmi jako je rekonstrukce obrazů, restaurace, segmentace, klasifikace, registrace (lícování) a fúze. Kromě obecného úvodu, pojednává tato disertační práce o dvou medicínsky orientovaných tématech, jež byla formulována ve spolupráci s Philips Netherland BV, divizí Philips Healthcare. První téma je zaměřeno na oblast zpracování obrazů subtrakční angiografie dolních končetin člověka získaných pomocí výpočetní X-Ray tomografie (CT). Subtrakční angiografie je obvykle využívaná při podezření na periferní cévní onemocnění (PAOD) nebo při akutním poškození dolních končetin jako jsou fraktury apod. Současné komerční metody nejsou dostatečně spolehlivé už v předzpracování, jako je například odstranění pacientského stolu, pokrývky, dlahy, apod. Spolehlivost a přesnost identifikace cév v subtrahovaných datech vedoucích v blízkosti kostí je v důsledku Partial Volume artefaktu rovněž nízká. Automatické odstranění kalcifikací nebo detekce malých cév doplňujících nezbytnou informaci o náhradním zásobení dolních končetin krví v případě přerušení hlavních zásobujících cév v současné době rovněž nesplňují kritéria pro plně automatické zpracování. Proto hlavním cílem týkající se tohoto tématu bylo vyvinout automatický systém, který by mohl současné nedostatky v CTSA vyšetření odstranit. Druhé téma je orientováno na identifikaci patologických změn na páteři člověka v CT obrazech se zaměřením na osteolytické a osteoblastické léze u jednotlivých obratlů. Tyto změny obvykle nastávají v důsledků postižení metastazujícím procesem rakovinového onemocnění. Pro detekci patologických změn je pak potřeba identifikace a segmentace jednotlivých obratlů. Přesnost analýzy jednotlivých lézí však závisí rovněž na správné identifikaci těla a zadních segmentů u jednotlivých obratlů a na segmentaci trabekulárního centra obratlů, tj. odstranění kortikální kosti. Během léčby mohou být pacienti skenováni vícekrát, obvykle s několika-mesíčním odstupem. Hodnocení případného vývoje již detekovaných patologických změn pak logicky vychází ze správné detekce patologií v jednotlivých obratlech korespondujících si v jednotlivých akvizicích. Jelikož jsou příslušné obratle v jednotlivých akvizicích obvykle na různé pozici, jejich fúze, vedoucí k analýze časového vývoje detekovaných patologií, je komplikovaná. Požadovaným výsledkem v tomto tématu je vytvoření komplexního systému pro detekci patologických změn v páteři, především osteoblastických a osteolytických lézí. Takový systém tedy musí umožnovat jak segmentaci jednotlivých obratlů, jejich automatické rozdělení na hlavní části a odstranění kortikální kosti, tak také detekci patologických změn a jejich hodnocení. Ačkoliv je tato disertační práce v obou výše zmíněných tématech primárně zaměřena na experimentální část zpracování medicínských obrazů, zabývá se všemi nezbytnými kroky, jako je předzpracování, registrace, dodatečné zpracování a hodnocení výsledků, vedoucími k možné aplikovatelnosti obou systému v klinické praxi. Jelikož oba systémy byly řešeny v rámci týmové spolupráce jako celek, u obou témat jsou pro některé konkrétní kroky uvedeny odkazy na doktorskou práci Miloše Malínského.Analysis of medical images has been subject of basic research for many years. Many research papers have been published in the field related to image analysis and focused on partial aspects such as reconstruction, restoration, segmentation and classification, registration (spatial alignment) and fusion. Besides the introduction of related general concepts used in medical image processing, this thesis deals with two specific medical problems formulated in cooperation with Philips Netherland BV, Philips Healthcare division. The first topic is focused on subtraction angiography in patients’ lower legs utilizing image data from X-Ray computed tomography (CT). CT subtraction angiography (CTSA) is typically used for indication of the Peripheral Artery Occlusive Disease (PAOD) and for examination of acute injuries of lower legs such as acute fractures, etc. Current methods in clinical praxis are not sufficient regarding the pre-processing such as masking of patient desk, cover, splint, etc. The subtraction of blood vessels adjacent to neighboring bones in lower legs is of low accuracy due to the Partial Volume artifact. Masking of calcifications and detection of tiny blood vessels complementing necessary information about the alternative blood supply in lower legs in case of obstruction in main arteries is also not reliable for fully automated process presently. Therefore, the main aim regarding this topic was to develop an automated framework that could overcome current shortcomings in CTSA examination. The second topic is oriented on the identification and evaluation of pathologic changes in human spine, focusing on osteolytic and osteoblastic lesions in individual vertebrae in CT images. Such changes occur typically as a consequence of metastasizing process of cancerous disease. For the detection of pathologic changes, an identification and segmentation of individual vertebrae is necessary. Moreover, the analysis of individual lesions in vertebrae depends also on correct identification of vertebral body and posterior segments of each vertebra, and on segmentation of their trabecular centers. Patients are typically examined more than once during their therapy. Then, the evaluation of possible tumorous progression is based on accurate detection of pathologies in individual vertebrae in the base-line and corresponding follow-up images. Since the corresponding vertebrae are in mutually different positions in the follow-up images, their fusion leading to the analysis of the lesion progression is complicated. The main aim regarding this topic is to develop a complex framework for detection of pathologic lesions on spine, with the main focus on osteoblastic and osteolystic lesions. Such system has to provide not only reliable segmentation of individual vertebrae and detection of their main regions but also the masking of their cortical bone, detection of their pathologic changes and their evaluation. Although this dissertation thesis is primarily oriented at the experimental part of medical image processing considering both the above mentioned topics, it deals with all necessary processing steps, i.e. preprocessing, image registration, post-processing and evaluation of results, leading to the future use of both frameworks in clinical practice. Since both frameworks were developed in a team, there are some chapters referring to the dissertation thesis of Milos Malinsky.