京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18452号 / 医博第3907号 / 新制||医||1004(附属図書館) / 31330 / 京都大学大学院医学研究科医学専攻 / (主査)教授 伊達 洋至, 教授 武田 俊一, 教授 富樫 かおり / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

In this dissertation, we approach three different contributions in measurement error model (MEM). Initially, we carry out maximum penalized likelihood inference in MEMs under the normality assumption. The methodology is based on the method proposed by Firth (1993), which can be used to improve some asymptotic properties of the maximum likelihood estimators. In the second contribution, we develop two new estimation methods based on generalized fiducial inference for the precision parameters and the variability product under the Grubbs model considering the two-instrument case. One method is based on a fiducial generalized pivotal quantity and the other one is built on the method of the generalized fiducial distribution. Comparisons with two existing approaches are reported. Finally, we propose to study inference in a heteroscedastic MEM with known error variances. Instead of the normal distribution for the random components, we develop a model that assumes a skew-t distribution for the true covariate and a centered Students t distribution for the error terms. The proposed model enables to accommodate skewness and heavy-tailedness in the data, while the degrees of freedom of the distributions can be different. We use the maximum likelihood method to estimate the model parameters and compute them via an EM-type algorithm. All proposed methodologies are assessed numerically through simulation studies and illustrated with real datasets extracted from the literature. / Neste trabalho abordamos três contribuições diferentes em modelos com erros de medição (MEM). Inicialmente estudamos inferência pelo método de máxima verossimilhança penalizada em MEM sob a suposição de normalidade. A metodologia baseia-se no método proposto por Firth (1993), o qual pode ser usado para melhorar algumas propriedades assintóticas de os estimadores de máxima verossimilhança. Em seguida, propomos construir dois novos métodos de estimação baseados na inferência fiducial generalizada para os parâmetros de precisão e a variabilidade produto no modelo de Grubbs para o caso de dois instrumentos. O primeiro método é baseado em uma quantidade pivotal generalizada fiducial e o outro é baseado no método da distribuição fiducial generalizada. Comparações com duas abordagens existentes são reportadas. Finalmente, propomos estudar inferência em um MEM heterocedástico em que as variâncias dos erros são consideradas conhecidas. Nós desenvolvemos um modelo que assume uma distribuição t-assimétrica para a covariável verdadeira e uma distribuição t de Student centrada para os termos dos erros. O modelo proposto permite acomodar assimetria e cauda pesada nos dados, enquanto os graus de liberdade das distribuições podem ser diferentes. Usamos o método de máxima verossimilhança para estimar os parâmetros do modelo e calculá-los através de um algoritmo tipo EM. Todas as metodologias propostas são avaliadas numericamente em estudos de simulação e são ilustradas com conjuntos de dados reais extraídos da literatura

Quantifying human posture and range of motion remains challenging due to the need for specific technologies, time for data collection and analysis, and space requirements. The demand for affordable and accessible human body position measurement requires alternative methods that cost less, are portable, and provide similar accuracy to expensive multi-camera systems. This thesis developed and evaluated a novel augmented reality mobile app for human posture measurement to bring marker-based body segment measurement to the point of patient contact. The augmented reality app provides live video of the person being measured, AprilTag2 fiducial markers locations in the video, processes marker data, and calculates angles and distances between markers. Results demonstrated that the mobile app can identify, track, and measure angles and distances between AprilTag2 markers attached to a human body in real-time with millimetre accuracy, thereby allowing researchers and clinicians to quantify posture measurements anywhere, at anytime.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19225号 / 医博第4024号 / 新制||医||1010(附属図書館) / 32224 / 京都大学大学院医学研究科医学専攻 / (主査)教授 戸井 雅和, 教授 富樫 かおり, 教授 一山 智 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM

This thesis presents methods for robust precision landing of unmanned air vehicles (UAVs) on platforms at sea. Localization methods are proposed for UAV-to-boat state estimation for systems that employ real- time kinematic (RTK) global navigation satellite system (GNSS) and vision sensors. Solutions for GNSS-only are first presented, followed by the fusion of GNSS and vision. The important problem of sensor intrinsic calibration is solved with a novel offline batch estimation approach. Hardware results are presented for all methods. Our calibration of GNSS-to-camera is shown to estimate sensor offsets with millimeter level accuracy. Localization systems are combined with custom state machines that manage the landing attempt via a novel descent cone. This conical threshold enforces a safe and accurate landing. Our landing methods are demonstrated in real-world experiments and achieve consistent accurate landings with error below 10 cm. The fusion of camera and RTK is shown to produce a robust landing system with redundant localization sources.

This thesis contributes to the topic of image-based feature localization and tracking in fluoroscopic (2D x-ray) image sequences. Such tracking is needed to automatically measure organ motion in cancer patients treated with radiation therapy. While the use of 3D cone-beam computed tomography (CBCT) images is a standard clinical practice for verifying the agreement of the patient's position to a plan, it is done before the treatment procedure. Hence, measurement of the motion during the procedure could improve plan design and the accuracy of treatment delivery. Using an existing CBCT imaging system is one way of collecting fluoroscopic sequences for such analysis. Since x-ray images of soft tissues are typically characterized with low contrast and high noise, radio-opaque fiducial markers are often inserted in or around the target. This thesis describes techniques that comprise a complete system for automated detection and tracking of the markers in fluoroscopic image sequences. One of the cornerstone design ideas in this thesis is the use of the 3D CBCT image of the patient, from which the markers can be extracted more easily, to initialize the tracking in the fluoroscopic image sequences. To do this, a specific marker-based image registration framework was proposed. It includes multiple novel techniques, such as marker segmentation and modelling, the marker enhancement filter, and marker-specific template image generation approaches. Through extensive experiments on testing data sets, these novel techniques were combined with appropriate state-of-the-art methods to produce a sleek, computationally efficient, fully automated system that achieved reliable marker localization and tracking. The accuracy of the system is sufficient for clinical implementation. The thesis demonstrates an application of the system to the images of prostate cancer patients, and includes examples of statistical analysis of organ motion that can be used to improve treatment planning. / Dissertation / Doctor of Philosophy (PhD) / This thesis presents the development of a software system that analyzes sequences of 2D x-ray images to automatically measure organ motion in patients undergoing radiation therapy for cancer treatment. The knowledge of motion statistics obtained from this system creates opportunities for patient-specific treatment design that may lead to a better outcome. Automated processing of organ motion is challenging due to the low contrast and high noise levels in the x-ray images. To achieve reliable detection, the proposed system was designed to make use of 3D cone-beam computed tomography images of the patient, where the features (markers) are easier to identify. This required the development of a specific image registration framework for aligning the images, including a number of novel feature modelling and image processing techniques. The proposed motion tracking approach was implemented as a complete software system that was extensively validated on phantom and patient studies. It achieved a level of accuracy and reliability that is suitable for clinical implementation.

Radiation therapy has a significant role in the management of cancer patients. Computed tomography (CT) has been at the forefront of radiation therapy planning due to its widespread diagnostic use and its electron density information. Magnetic resonance (MR) imaging is another proven diagnostic modality, which can achieve superior soft tissue contrast and margin delineation, relative to CT. As such it has become a valuable tool for cancer diagnoses and staging. In this study, a centre-out radial acquisition using an off-resonance reception (co-RASOR) MR sequence, sensitive to magnetic field inhomogeneity, was applied to excite a broader frequency range of spins in the vicinity of metallic seeds. The resultant images display local hyperintensities around metallic markers. These images were then reconstructed with frequency offsets to rewind these hyperintensities to the geometric centre to obtain positive contrast. The contrast-to-noise ratio (CNR) was measured between a fiducial and its surrounding to compare Fourier and iterative reconstruction methods for undersampled co-RASOR. The motivation was to reduce the sequence acquisition time, while preserving sufficient CNR and resolution. For single slices, acquisition was 2.8 sec and multi-slice acquisition could acquire more than 50 slices in 73 sec, by reducing the acquired data by a factor of 8. This effectively encodes acquisition to 1.4 sec/slice. The noise present in undersampled images decreased significantly using iterative reconstruction methods, but a total variation based penalty better preserved the edges. Further extensions to the reconstruction method applied frequency-based filters that could isolate signals from different metallic compounds. The local hyperintensities rewind using opposite signed frequency offsets for diamagnetic and paramagnetic seeds. This allowed individual visualization of a low dose rate (LDR) brachytherapy seed and a gold fiducial marker. Phantom validation showed that each seed contains its maximal CNR in opposing frequency regions. The relative difference between global and local maxima in each frequency band ranged from 1.19 -- 3.73, and a single cut-off frequency was successfully applied for each acquisition plane. Image guidance systems rely on the position of these fiducial markers to compare daily setup images with CT and MR simulations. Phantom experiments and porcine tissue samples were used to assess the minimum separation of fiducials, geometric accuracy, and random errors associated with using the co-RASOR sequence. co-RASOR images were able to resolve fiducials separated by 0.5 - 1 cm, depending on image resolution. No systematic biases were observed by comparing co-RASOR to CT using rigid body registrations. The standard deviation of the systematic errors were \textless 0.5 mm between MR and CT registrations, and \textless 0.4 mm between MR scans without CT. These values are smaller than the current total systematic uncertainties, which should be limimed to <3 mm. The methods presented here can aid in understanding the trade-offs between acquisition speed and signal properties, differentiating cases where brachytherapy seeds are used in combination with fiducial markers for external beam boost, and aid in co-registration of multimodality imaging. / Thesis / Doctor of Philosophy (PhD)

Stereopsis or Stereo vision is a technique that has been extensively used in computer vision these days helps to percept the 3D structure and distance of a scene from two images taken at different viewpoints, precisely the same way a human being visualizes anything using both eyes. The research involves object matching by extracting features from images and includes some preliminary tasks like camera calibration, correspondence and reconstruction of images taken by a stereo vision unit and 3D construction of an object. The main goal of this research work is to estimate the position and the orientation of a front loader bucket of an autonomous mobile robot configured in a work machine name 'Avant', which consists a stereo vision unit and several other sensors and is designed for outdoor operations like excavation. Several image features finding algorithms, including the most prominent two, SIFT and SURF has been considered for the image matching and object recognition. Both algorithms find interest points in an image in different ways which apparently accelerates the feature extraction procedure, but still the time requires for matching in both cases is left as an important issue to be resolved. As the machine requires to do some loading and unloading tasks, dust and other particles could be a major obstacle for recognizing the bucket at workspace, also it has been observed that the hydraulic arm and other equipment comes inside the FOV of the cameras which also makes the task much challenging. The concept of using markers has been considered as a solution to these problems. Moreover, the outdoor environment is very different from indoor environment and object matching is far more challenging due to some factors like light, shadows, environment, etc. that change the features inside a scene very rapidly. Although the work focuses on position and orientation estimation, optimum utilization of stereo vision like environment perception or ground modeling can be an interesting avenue of future research / <p>Validerat; 20101230 (ysko)</p>

PURPOSE: Thermal ablation therapy is an emerging local cancer treatment to destroy cancer tissue using heat. However variations in blood flow and energy absorption rates make it extremely challenging to monitor thermal changes. Insufficient ablation may lead to recurrence of the cancer while excessive ablation may damage adjacent healthy tissues. Ultrasound could be a convenient and inexpensive imaging modality for real-time monitoring of the ablation. For the development and optimization of these methods, it is essential to have ground truth data and a reliable and quantitative validation technique before beginning clinical trials on humans. In this dissertation, my primary focus was to solve the image-to-physical space registration problem using stereotactic fiducials that provide accurate correlation of ultrasound and pathology (ground truth) images. METHOD: A previously developed validation test-bed prototype was evaluated using phantom experiments to identify the shortcomings and limitations. In order to develop an improved validation platform, a simulator was implemented for evaluating registration methods as well as different line fiducial structures. New fiducial line structures were proposed, and new methods were implemented to overcome the limitations of the old system. The new methods were then tested using simulation results and phantom studies. Phantom experiments were conducted to improve the visibility of fiducials, as well as the quality of acquired ultrasound and pathology image datasets. RESULTS: The new system outperforms the previous one in terms of accuracy, robustness, and simplicity. The new registration method is robust to missing fiducials. I also achieved complete fiducial visibility in all images. Enhancing the tissue fixation medium improved the ultrasound data quality. The quality of pathology images were improved by a new imaging method. Simulation results show improvement in pose recovery accuracy using my proposed fiducial structure. This was validated by phantom studies reducing spatial misalignment between the US and pathology image sets. CONCLUSION: A new generation of test-bed was developed that provides a reliable and quantitative validation technique for evaluating and optimizing ablation monitoring methods. / Thesis (Master, Computing) -- Queen's University, 2011-09-29 20:31:55.159

Le cancer de la prostate est le cancer le plus fréquent chez l'homme en Europe, néanmoins il n'existe actuellement pas de technique d'imagerie permettant de détecter avec précision les tumeurs dans la glande. Sachant que les coupes histologiques contiennent la réalité de terrain concernant le diagnostic, il est nécessaire de recaler les images de chaque technique d'imagerie aux coupes histologiques afin de pouvoir les évaluer. De plus, comme il n'existe pas de méthode permettant de contrôler précisément le plan de coupe histologique, le recalage doit être considéré comme un problème 3D. Un dispositif permettant de réaliser, de manière rapide et standardisée, des marqueurs internes dans les coupes histologiques a été développé, de même qu'un algorithme permettant de détecter automatiquement ces marqueurs, de les identifier et d'aligner les coupes histologiques. La méthode a été testée sur 10 prostates, avec en moyenne 19.2 coupes par prostate, et a permis d'obtenir une précision de recalage moyenne de 0.18 ± 0.13 mm au niveau des marqueurs. Un deuxième algorithme a été développé pour recaler les coupes histologiques, une fois alignées, avec les images IRM. Ce recalage a été conçu pour être guidé par les canaux éjaculateurs, un repère anatomique présent dans chaque prostate et visible à la fois en histologie et dans les images IRM cliniques, acquises avec une résolution standard. L'algorithme a d'abord été testé en s'appuyant sur les marqueurs artificiels. La précision obtenue pour le recalage était en moyenne de 0.45±0.25 mm au niveau des marqueurs et de 1.04 ± 0.21 mm au niveau des canaux éjaculateurs. L'algorithme a enfin été testé en guidant le recalage à l'aide de la position des canaux éjaculateurs. La précision moyenne obtenue était alors de 0.16±0.05 mm au niveau des canaux éjaculateurs et de 2.82±0.41 mm au niveau des marqueurs. Ces résultats suggèrent une valeur du facteur de rétrécissement de l'ordre de 1.07±0.03 et une inclinaison vis à vis du plan de coupe histologique de l'ordre de 13.6◦ ± 9.61◦, avec une variance importante pour ces deux paramètres / Prostate cancer is the most frequently diagnosed cancer of men in Europe, yet no current imaging technique is capable of detecting with precision tumours in the prostate. The histology slices are the gold standard for the diagnosis. Therefore, in order to evaluate each imaging technique, the histology slices must be precisely registered to the imaged data. As it cannot be assumed that the histology slices are cut along the same plane as the imaged data is acquired, the registration must be considered as a 3D problem. An apparatus has been developed that enables internal fiducial markers to be created in the histology slices in a rapid and standardised manner. An algorithm has been developed that automatically detects and identifies these markers, enabling the alignment of the histology slices. The method has been tested on 10 prostate specimens, with 19.2 slices on average per specimen. The accuracy of the alignment at the fiducial markers was on average 0.18±0.13 mm. A second algorithm was developed to 3D register the aligned histology slices with the MR images. The registration is designed to be guided by the ejaculatory ducts, an anatomical landmark present in every prostate and visible in both histology and MR images acquired at standard clinical resolution. The algorithm was first tested by using the fiducial needles to guide the registration. The average registration accuracy was 0.45 ± 0.25 mm at the fiducial needles and 1.04±0.21 mm at the ejaculatory ducts. The algorithm was then tested by using the ejaculatory ducts to guide the registration. The average registration accuracy was 0.16±0.05 mm at the ejaculatory ducts and 2.82 ± 0.41 mm at the fiducial needles. The results suggest that the histology shrinkage factor is of the order 1.07±0.03 and the tilt of the histology slicing plane is 13.6◦ ±9.61◦, with both parameters showing significant variance