Segmentación y registración de imágenes 3d

La generación de modelos de superficie, herramientas que facilitan el manejo de información 3D, técnicas de adquisición, generación y visualización de modelos 3D, entre otros, han venido a conformar la Estereología. El tratamiento de imágenes 3D, segmentación, generación de modelos numéricos de superficies, y en particular en imágenes médicas, registración, i.e. integración del Atlas Cerebral u otro atlas anatómicos con las imágenes, requiere el conocimiento de herramientas conceptuales y algorítmicas útiles en muchas otras aplicaciones 3D.Eje: VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Segmentación y registración de imágenes 3d

La generación de modelos de superficie, herramientas que facilitan el manejo de información 3D, técnicas de adquisición, generación y visualización de modelos 3D, entre otros, han venido a conformar la Estereología. El tratamiento de imágenes 3D, segmentación, generación de modelos numéricos de superficies, y en particular en imágenes médicas, registración, i.e. integración del Atlas Cerebral u otro atlas anatómicos con las imágenes, requiere el conocimiento de herramientas conceptuales y algorítmicas útiles en muchas otras aplicaciones 3D.Eje: VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Atlas-based segmentation and classification of magnetic resonance brain images

A wide range of different image modalities can be found today in medical imaging. These modalities allow the physician to obtain a non-invasive view of the internal organs of the human body, such as the brain. All these three dimensional images are of extreme importance in several domains of medicine, for example, to detect pathologies, follow the evolution of these pathologies, prepare and realize surgical planning with, or without, the help of robot systems or for statistical studies. Among all the medical image modalities, Magnetic Resonance (MR) imaging has become of great interest in many research areas due to its great spatial and contrast image resolution. It is therefore perfectly suited for anatomic visualization of the human body such as deep structures and tissues of the brain. Medical image analysis is a complex task because medical images usually involve a large amount of data and they sometimes present some undesirable artifacts, as for instance the noise. However, the use of a priori knowledge in the analysis of these images can greatly simplify this task. This prior information is usually represented by the reference images or atlases. Modern brain atlases are derived from high resolution cryosections or in vivo images, single subject-based or population-based, and they provide detailed images that may be interactively and easily examined in their digital format in computer assisted diagnosis or intervention. Then, in order to efficiently combine all this information, a battery of registration techniques is emerging based on transformations that bring two medical images into voxel-to-voxel correspondence. One of the main aims of this thesis is to outline the importance of including prior knowledge in the medical image analysis framework and the indispensable role of registration techniques in this task. In order to do that, several applications using atlas information are presented. First, the atlas-based segmentation in normal anatomy is shown as it is a key application of medical image analysis using prior knowledge. It consists of registering the brain images derived from different subjects and modalities within the atlas coordinate system to improve the localization and delineation of the structures of interest. However, the use of an atlas can be problematic in some particular cases where some structures, for instance a tumor or a sulcus, exists in the subject and not in the atlas. In order to solve this limitation of the atlases, a new atlas-based segmentation method for pathological brains is proposed in this thesis as well as a validation method to assess this new approach. Results show that deep structures of the brain can still be efficiently segmented using an anatomic atlas even if they are largely deformed because of a lesion. The importance of including a priori knowledge is also presented in the application of brain tissue classification. The prior information represented by the tissue templates can be included in a brain tissue segmentation approach thanks to the registration techniques. This is another important issue presented in this thesis and it is analyzed through a comparative study of several non-supervised classification techniques. These methods are selected to represent the whole range of prior information that can be used in the classification process: the image intensity, the local spatial model, and the anatomical priors. Results show that the registration between the subject and the tissue templates allows the use of prior information but the accuracy of both the prior information and the registration highly influence the performance of the classification techniques. Another aim of this thesis is to present the concept of dynamic medical image analysis, in which the prior knowledge and the registration techniques are also of main importance. Actually, many medical image applications have the objective of statically analyzing one single image, as for instance in the case of atlas-based segmentation or brain tissue classification. But in other cases the implicit idea of changes detection is present. Intuitively, since the human body is changing continuously, we would like to do the image analysis from a dynamic point of view by detecting these changes, and by comparing them afterwards with templates to know if they are normal. The need of such approaches is even more evident in the case of many brain pathologies such as tumors, multiple sclerosis or degenerative diseases. In these cases, the key point is not only to detect but also to quantify and even characterize the evolving pathology. The evaluation of lesion variations over time can be very useful, for instance in the pharmaceutical research and clinical follow up. Of course, a sequence of images is needed in order to do such an analysis. Two approaches dealing with the idea of change detection are proposed as the last (but not least) issue presented in this work. The first one consists of performing a static analysis of each image forming the data set and, then, of comparing them. The second one consists of analyzing the non-rigid transformation between the sequence images instead of the images itself. Finally, both static and dynamic approaches are illustrated with a potential application: the cortical degeneration study is done using brain tissue segmentation, and the study of multiple sclerosis lesion evolution is performed by non-rigid deformation analysis. In conclusion, the importance of including a priori information encoded in the brain atlases in medical image analysis has been put in evidence with a wide range of possible applications. In the same way, the key role of registration techniques is shown not only as an efficient way to combine all the medical image modalities but also as a main element in the dynamic medical image analysis

Using compartment models of diffusion MRI to investigate the preterm brain

Preterm birth is the leading cause of neonatal mortality, with survivors experiencing motor, cognitive and other deficits at increased rates. In preterm infancy, the developing brain undergoes folding, myelination, and rapid cellular growth. Diffusion-Weighted Magnetic Resonance Imaging (DW MRI) is an imaging modality that allows noninvasive inference of cellular microstructure in living tissue, and its parameters reflect changes in brain tissue composition. In this thesis, we employ compartment models of DW MRI to investigate the microstructure in preterm-born subjects at different ages. Within infants, we have used the NODDI model to investigate longitudinal changes in neurite density and orientation dispersion within the white matter, cerebral cortex and thalamus, explaining known trends in diffusion tensor parameters with greater specificity. We then used a quantitative T2 sequence to develop and investigate a novel, multi-modal parameter known as the ‘g-ratio’. We have also investigated changing microstructural geometry within the cortex. Immediately after preterm birth, the highly-ordered underlying cellular structure makes diffusion in the cortex almost entirely radial. This undergoes a transition to a disordered and isotropic state over the first weeks of life, which we have used the DIAMOND model to quantify. This radiality decreases at a rate that depends on the cortical lobe. In a cohort of young adults born extremely preterm, we have quantified differences in brain microstructure compared to term-born controls. In preterm subjects, the brain structures are smaller than for controls, leading to increased partial volume in some regions of interest. We introduce a method to infer diffusion parameters in partial volume, even for regions which are smaller than the diffusion resolution. Overall, this thesis utilises and evaluates a variety of compartment models of DW MRI. By developing and applying principled and robust methodology, we present new insights into microstructure within the preterm-born brain

Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)