Division-Based Methods For Large Point Sets Registration

Pendaftaran set titik adalah satu langkah penting untuk mengukur persamaan antara dua set titik dan digunakan secara meluas dalam penglihatan komputer, grafik komputer, analisis imej perubatan, dan sebagainya. Peralatan semasa mampu menyediakan data dengan butiran terperinci sebagai set titik besar. Walau bagaimanapun, prestasi kaedah pendaftaran konvensional menurun secara mendadak apabila saiz set titik meningkat. Dalam tesis ini, tiga kaedah pendaftaran set titik terkenal dan antara yang mempunyai prestasi terbaik dipertimbangkan untuk mengkaji pengubahan kaedah konvensional kepada kaedah yang menangani pendaftaran set titik besar dengan cekap. Kaedah-kaedah tersebut adalah Lelaran Titik Terdekat (ICP), Peralihan Titik Bersambung (CPD) dan Model Campuran Gaussian berasaskan Plat-nipis Splin. Point sets registration is a key step for measuring the similarity between two point sets and widely used in various fields such as computer vision, computer graphics, medical image analysis, to name a few. The current devices can capture data with great details as large point set. However, conventional registration methods slow down dramatically as the size of the point set increased. In this thesis, three well-known and among-best-performance point sets registration methods incorporating division schemes are considered to study transforming conventional methods to efficiently deal with large point sets registration. These methods are Iterative Closest Point (ICP), Coherent Point Drift (CPD), and Gaussian mixture models based on thin-plate splines (GMM-TPS)

Statistical Modelling of Craniofacial Shape

With prior knowledge and experience, people can easily observe rich shape and texture variation for a certain type of objects, such as human faces, cats or chairs, in both 2D and 3D images. This ability helps us recognise the same person, distinguish different kinds of creatures and sketch unseen samples of the same object class. The process of capturing this prior knowledge is mathematically interpreted as statistical modelling. The outcome is a morphable model, a vector space representation of objects, that captures the variation of shape and texture. This thesis presents research aimed at constructing 3DMMs of craniofacial shape and texture using new algorithms and processing pipelines to offer enhanced modelling abilities over existing techniques. In particular, we present several fully automatic modelling approaches and apply them to a large dataset of 3D images of the human head, the Headspace dataset, thus generating the first public shape-and- texture 3D Morphable Model (3DMM) of the full human head. We call this the Liverpool-York Head Model, reflecting the data collection and statistical modelling respectively. We also explore the craniofacial symmetry and asymmetry in template morphing and statistical modelling. We propose a Symmetry-aware Coherent Point Drift (SA-CPD) algorithm, which mitigates the tangential sliding problem seen in competing morphing algorithms. Based on the symmetry-constrained correspondence output of SA-CPD, we present a symmetry-factored statistical modelling method for craniofacial shape. Also, we propose an iterative process of refinement for a 3DMM of the human ear that employs data augmentation. Then we merge the proposed 3DMMs of the ear with the full head model. As craniofacial clinicians like to look at head profiles, we propose a new pipeline to build a 2D morphable model of the craniofacial sagittal profile and augment it with profile models from frontal and top-down views. Our models and data are made publicly available online for research purposes

Light Sheet Microscopy and Image Analysis of Neural Development and Programmed Cell Death in C. Elegans Embryos

The positioning of neuronal cell bodies and neurites is critical for intact functioning of the nervous system. Mapping the positions of the soma and neurites in the brains of developing embryos as important central nervous system structures are being created may yield novel insight into the role of distinct cell groups in creating these structures. New developments in microscopy have made this an excellent time to study neural development in the C. elegans embryo. In the past decade, implementations of highly light efficient methods such as single plane illumination microscopy have rendered it possible to follow development of embryonic structures in 3D with excellent temporal resolution (Huisken et al., 2004) and low phototoxicity. Recent work has resulted in quantitative characterization of the outgrowth of a single neurite in the late, rapidly moving three-fold stage of the C. elegans embryo for the first time (Christensen et al., 2015). In this thesis, I first describe the construction and programming of a single plane illumination microscope (SPIM) based on a design from Hari Shroff\u27s lab (Wu et al., 2011). The microscope is developed especially for use with C. elegans embryos and permits fast image acquisition without excessive photodamage, compared to other forms of microscopy. Second, I describe the use of the SPIM microscope to image the development of a subset of sublateral neurons, the earliest known entrants to the nerve ring (Rapti et al, in preparation), into which they grow in the 1.5-fold stage. I describe an algorithm for automatically aligning developing embryos onto one another until the beginning of the rapid embryonic movements known as twitching, which begin at the start of the twofold stage. I employ my algorithm to align a group of identically imaged embryos onto one another and deduce information about the positioning of the nerve ring in an approximately uniform coordinate system. I determine that nerve rings are precisely positioned in the embryo to within about a micrometer while the cell bodies that grow into the nerve ring are positioned over a much wider distance. My work suggests that the nerve ring grows out towards the ALA neuron as an anchor, and that twitching may begin when the developing nerve ring reaches the ALA. I additionally describe observation of new phenotypes related to the cam-1 mutation, which was previously identified as a regulator of anterior-posterior placement of the nerve ring (Kennerdell et al., 2009). Third, I describe an application of the SPIM microscope for imaging the death of the tail spike cell, a complex, multi-compartment differentiated cell which dies over a period of hours during the three-fold stage, when the animal is rapidly moving in its shell, and cannot be imaged otherwise than with a rapid, light efficient microscope such as the one described here. I determined the time course and confirmed the sequence of events of wild type tail spike cell death. Additionally, I report stronger phenotypes for some known tail spike cell death genes when imaged in the embryo, suggesting that eff-1 plays a stronger role than previously known in clearance of the distal part of the tail spike cell process, and additionally that ced-5 has a strong role in clearance of the same compartment (in addition to its known role in soma clearance). In an appendix I describe work beginning on an extension of the microscope, which will hopefully see the microscope used as a tool for selectively inducing fluorescence in individual cells and following the development of those cells in time. My results demonstrate the utility of single plane illumination microscopy for study of C. elegans embryogenesis and establish fundamental facts about the variability of the C. elegans central nervous system by making direct comparisons between animals. This work contributes to our understanding of the C. elegans nervous system by establishing fundamental bounds on the range of nerve ring positioning between individuals

Advanced tracking and image registration techniques for intraoperative radiation therapy

Mención Internacional en el título de doctorIntraoperative electron radiation therapy (IOERT) is a technique used to deliver radiation to the surgically opened tumor bed without irradiating healthy tissue. Treatment planning systems and mobile linear accelerators enable clinicians to optimize the procedure, minimize stress in the operating room (OR) and avoid transferring the patient to a dedicated radiation room. However, placement of the radiation collimator over the tumor bed requires a validation methodology to ensure correct delivery of the dose prescribed in the treatment planning system. In this dissertation, we address three well-known limitations of IOERT: applicator positioning over the tumor bed, docking of the mobile linear accelerator gantry with the applicator and validation of the dose delivery prescribed. This thesis demonstrates that these limitations can be overcome by positioning the applicator appropriately with respect to the patient’s anatomy. The main objective of the study was to assess technological and procedural alternatives for improvement of IOERT performance and resolution of problems of uncertainty. Image-to-world registration, multicamera optical trackers, multimodal imaging techniques and mobile linear accelerator docking are addressed in the context of IOERT. IOERT is carried out by a multidisciplinary team in a highly complex environment that has special tracking needs owing to the characteristics of its working volume (i.e., large and prone to occlusions), in addition to the requisites of accuracy. The first part of this dissertation presents the validation of a commercial multicamera optical tracker in terms of accuracy, sensitivity to miscalibration, camera occlusions and detection of tools using a feasible surgical setup. It also proposes an automatic miscalibration detection protocol that satisfies the IOERT requirements of automaticity and speed. We show that the multicamera tracker is suitable for IOERT navigation and demonstrate the feasibility of the miscalibration detection protocol in clinical setups. Image-to-world registration is one of the main issues during image-guided applications where the field of interest and/or the number of possible anatomical localizations is large, such as IOERT. In the second part of this dissertation, a registration algorithm for image-guided surgery based on lineshaped fiducials (line-based registration) is proposed and validated. Line-based registration decreases acquisition time during surgery and enables better registration accuracy than other published algorithms. In the third part of this dissertation, we integrate a commercial low-cost ultrasound transducer and a cone beam CT C-arm with an optical tracker for image-guided interventions to enable surgical navigation and explore image based registration techniques for both modalities. In the fourth part of the dissertation, a navigation system based on optical tracking for the docking of the mobile linear accelerator to the radiation applicator is assessed. This system improves safety and reduces procedure time. The system tracks the prescribed collimator location to solve the movements that the linear accelerator should perform to reach the docking position and warns the user about potentially unachievable arrangements before the actual procedure. A software application was implemented to use this system in the OR, where it was also evaluated to assess the improvement in docking speed. Finally, in the last part of the dissertation, we present and assess the installation setup for a navigation system in a dedicated IOERT OR, determine the steps necessary for the IOERT process, identify workflow limitations and evaluate the feasibility of the integration of the system in a real OR. The navigation system safeguards the sterile conditions of the OR, clears the space available for surgeons and is suitable for any similar dedicated IOERT OR.La Radioterapia Intraoperatoria por electrones (RIO) consiste en la aplicación de radiación de alta energía directamente sobre el lecho tumoral, accesible durante la cirugía, evitando radiar los tejidos sanos. Hoy en día, avances como los sistemas de planificación (TPS) y la aparición de aceleradores lineales móviles permiten optimizar el procedimiento, minimizar el estrés clínico en el entorno quirúrgico y evitar el desplazamiento del paciente durante la cirugía a otra sala para ser radiado. La aplicación de la radiación se realiza mediante un colimador del haz de radiación (aplicador) que se coloca sobre el lecho tumoral de forma manual por el oncólogo radioterápico. Sin embargo, para asegurar una correcta deposición de la dosis prescrita y planificada en el TPS, es necesaria una adecuada validación de la colocación del colimador. En esta Tesis se abordan tres limitaciones conocidas del procedimiento RIO: el correcto posicionamiento del aplicador sobre el lecho tumoral, acoplamiento del acelerador lineal con el aplicador y validación de la dosis de radiación prescrita. Esta Tesis demuestra que estas limitaciones pueden ser abordadas mediante el posicionamiento del aplicador de radiación en relación con la anatomía del paciente. El objetivo principal de este trabajo es la evaluación de alternativas tecnológicas y procedimentales para la mejora de la práctica de la RIO y resolver los problemas de incertidumbre descritos anteriormente. Concretamente se revisan en el contexto de la radioterapia intraoperatoria los siguientes temas: el registro de la imagen y el paciente, sistemas de posicionamiento multicámara, técnicas de imagen multimodal y el acoplamiento del acelerador lineal móvil. El entorno complejo y multidisciplinar de la RIO precisa de necesidades especiales para el empleo de sistemas de posicionamiento como una alta precisión y un volumen de trabajo grande y propenso a las oclusiones de los sensores de posición. La primera parte de esta Tesis presenta una exhaustiva evaluación de un sistema de posicionamiento óptico multicámara comercial. Estudiamos la precisión del sistema, su sensibilidad a errores cometidos en la calibración, robustez frente a posibles oclusiones de las cámaras y precisión en el seguimiento de herramientas en un entorno quirúrgico real. Además, proponemos un protocolo para la detección automática de errores por calibración que satisface los requisitos de automaticidad y velocidad para la RIO demostrando la viabilidad del empleo de este sistema para la navegación en RIO. Uno de los problemas principales de la cirugía guiada por imagen es el correcto registro de la imagen médica y la anatomía del paciente en el quirófano. En el caso de la RIO, donde el número de posibles localizaciones anatómicas es bastante amplio, así como el campo de trabajo es grande se hace necesario abordar este problema para una correcta navegación. Por ello, en la segunda parte de esta Tesis, proponemos y validamos un nuevo algoritmo de registro (LBR) para la cirugía guiada por imagen basado en marcadores lineales. El método propuesto reduce el tiempo de la adquisición de la posición de los marcadores durante la cirugía y supera en precisión a otros algoritmos de registro establecidos y estudiados en la literatura. En la tercera parte de esta tesis, integramos un transductor de ultrasonido comercial de bajo coste, un arco en C de rayos X con haz cónico y un sistema de posicionamiento óptico para intervenciones guiadas por imagen que permite la navegación quirúrgica y exploramos técnicas de registro de imagen para ambas modalidades. En la cuarta parte de esta tesis se evalúa un navegador basado en el sistema de posicionamiento óptico para el acoplamiento del acelerador lineal móvil con aplicador de radiación, mejorando la seguridad y reduciendo el tiempo del propio acoplamiento. El sistema es capaz de localizar el colimador en el espacio y proporcionar los movimientos que el acelerador lineal debe realizar para alcanzar la posición de acoplamiento. El sistema propuesto es capaz de advertir al usuario de aquellos casos donde la posición de acoplamiento sea inalcanzable. El sistema propuesto de ayuda para el acoplamiento se integró en una aplicación software que fue evaluada para su uso final en quirófano demostrando su viabilidad y la reducción de tiempo de acoplamiento mediante su uso. Por último, presentamos y evaluamos la instalación de un sistema de navegación en un quirófano RIO dedicado, determinamos las necesidades desde el punto de vista procedimental, identificamos las limitaciones en el flujo de trabajo y evaluamos la viabilidad de la integración del sistema en un entorno quirúrgico real. El sistema propuesto demuestra ser apto para el entorno RIO manteniendo las condiciones de esterilidad y dejando despejado el campo quirúrgico además de ser adaptable a cualquier quirófano similar.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Raúl San José Estépar.- Secretario: María Arrate Muñoz Barrutia.- Vocal: Carlos Ferrer Albiac

Contributions of biomechanical modeling and machine learning to the automatic registration of Multiparametric Magnetic Resonance and Transrectal Echography for prostate brachytherapy

El cáncer de próstata (CaP) es el primer cáncer por incidencia en hombres en países occidentales, y el tercero en mortalidad. Tras detectar en sangre una elevación del Antígeno Prostático Específico (PSA) o tras tacto rectal sospechoso se realiza una Resonancia Magnética (RM) de la próstata, que los radiólogos analizan para localizar las regiones sospechosas. A continuación, estas se biopsian, es decir, se toman muestras vivas que posteriormente serán analizadas histopatológicamente para confirmar la presencia de cáncer y establecer su grado de agresividad. Durante la biopsia se emplea típicamente Ultrasonidos (US) para el guiado y la localización de las lesiones. Sin embargo, estas no son directamente visibles en US, y el urólogo necesita usar software de fusión que realice un registro RM-US que transfiera la localizaciones marcadas en MR al US. Esto es fundamental para asegurar que las muestras tomadas provienen verdaderamente de la zona sospechosa. En este trabajo se compendian cinco publicaciones que emplean diversos algoritmos de Inteligencia Artificial (IA) para analizar las imágenes de próstata (RM y US) y con ello mejorar la eficiencia y precisión en el diagnóstico, biopsia y tratamiento del CaP: 1. Segmentación automática de próstata en RM y US: Segmentar la próstata consiste en delimitar o marcar la próstata en una imagen médica, separándola del resto de órganos o estructuras. Automatizar por completo esta tarea, que es previa a todo análisis posterior, permite ahorrar un tiempo significativo a radiólogos y urólogos, mejorando también la precisión y repetibilidad. 2. Mejora de la resolución de segmentación: Se presenta una metodología para mejorar la resolución de las segmentaciones anteriores. 3. Detección y clasificación automática de lesiones en RM: Se entrena un modelo basado en IA para detectar las lesiones como lo haría un radiólogo, asignándoles también una estimación del riesgo. Se logra mejorar la precisión diagnóstica, dando lugar a un sistema totalmente automático que podría implantarse para segunda opinión clínica o como criterio para priorización. 4. Simulación del comportamiento biomecánico en tiempo real: Se propone acelerar la simulación del comportamiento biomecánico de órganos blandos mediante el uso de IA. 5. Registro automático RM-US: El registro permite localizar en US las lesiones marcadas en RM. Una alta precisión en esta tarea es esencial para la corrección de la biopsia y/o del tratamiento focal del paciente (como braquiterapia de alta tasa). Se plantea el uso de la IA para resolver el problema de registro en tiempo casi real, utilizando modelos biomecánicos subyacentes.Prostate cancer (PCa) is the most common malignancy in western males, and third by mortality. After detecting elevated Prostate Specific Antigen (PSA) blood levels or after a suspicious rectal examination, a Magnetic Resonance (MR) image of the prostate is acquired and assessed by radiologists to locate suspicious regions. These are then biopsied, i.e. living tissue samples are collected and analyzed histopathologically to confirm the presence of cancer and establish its degree of aggressiveness. During the biopsy procedure, Ultrasound (US) is typically used for guidance and lesion localization. However, lesions are not directly visible in US, and the urologist needs to use fusion software to performs MR-US registration, so that the MR-marked locations can be transferred to the US image. This is essential to ensure that the collected samples truly come from the suspicious area. This work compiles five publications employing several Artificial Intelligence (AI) algorithms to analyze prostate images (MR and US) and thereby improve the efficiency and accuracy in diagnosis, biopsy and treatment of PCa: 1. Automatic prostate segmentation in MR and US: Prostate segmentation consists in delimiting or marking the prostate in a medical image, separating it from the rest of the organs or structures. Automating this task fully, which is required for any subsequent analysis, saves significant time for radiologists and urologists, while also improving accuracy and repeatability. 2. Segmentation resolution enhancement: A methodology for improving the resolution of the previously obtained segmentations is presented. 3. Automatic detection and classification of MR lesions: An AI model is trained to detect lesions as a radiologist would and to estimate their risk. The model achieves improved diagnostic accuracy, resulting in a fully automatic system that could be used as a second clinical opinion or as a criterion for patient prioritization. 4. Simulation of biomechanical behavior in real time: It is proposed to accelerate the simulation of biomechanical behavior of soft organs using AI. 5. Automatic MR-US registration: Registration allows localization of MR-marked lesions on US. High accuracy in this task is essential for the correctness of the biopsy and/or focal treatment procedures (such as high-rate brachytherapy). Here, AI is used to solve the registration problem in near-real time, while exploiting underlying biomechanically-compatible models

Automatic analysis of medical images for change detection in prostate cancer

Prostate cancer is the most common cancer and second most common cause of cancer death in men in the UK. However, the patient risk from the cancer can vary considerably, and the widespread use of prostate-specific antigen (PSA) screening has led to over-diagnosis and over-treatment of low-grade tumours. It is therefore important to be able to differentiate high-grade prostate cancer from the slowly- growing, low-grade cancer. Many of these men with low-grade cancer are placed on active surveillance (AS), which involves constant monitoring and intervention for risk reclassification, relying increasingly on magnetic resonance imaging (MRI) to detect disease progression, in addition to TRUS-guided biopsies which are the routine clinical standard method to use. This results in a need for new tools to process these images. For this purpose, it is important to have a good TRUS-MR registration so corresponding anatomy can be located accurately between the two. Automatic segmentation of the prostate gland on both modalities reduces some of the challenges of the registration, such as patient motion, tissue deformation, and the time of the procedure. This thesis focuses on the use of deep learning methods, specifically convolutional neural networks (CNNs), for prostate cancer management. Chapters 4 and 5 investigated the use of CNNs for both TRUS and MRI prostate gland segmentation, and reported high segmentation accuracies for both, Dice Score Coefficients (DSC) of 0.89 for TRUS segmentations and DSCs between 0.84-0.89 for MRI prostate gland segmentation using a range of networks. Chapter 5 also investigated the impact of these segmentation scores on more clinically relevant measures, such as MRI-TRUS registration errors and volume measures, showing that a statistically significant difference in DSCs did not lead to a statistically significant difference in the clinical measures using these segmentations. The potential of these algorithms in commercial and clinical systems are summarised and the use of the MRI prostate gland segmentation in the application of radiological prostate cancer progression prediction for AS patients are investigated and discussed in Chapter 8, which shows statistically significant improvements in accuracy when using spatial priors in the form of prostate segmentations (0.63 ± 0.16 vs. 0.82 ± 0.18 when comparing whole prostate MRI vs. only prostate gland region, respectively)