Automated Extraction of Biomarkers for Alzheimer's Disease from Brain Magnetic Resonance Images

In this work, different techniques for the automated extraction of biomarkers for Alzheimer's disease (AD) from brain magnetic resonance imaging (MRI) are proposed. The described work forms part of PredictAD (www.predictad.eu), a joined European research project aiming at the identification of a unified biomarker for AD combining different clinical and imaging measurements. Two different approaches are followed in this thesis towards the extraction of MRI-based biomarkers: (I) the extraction of traditional morphological biomarkers based on neuronatomical structures and (II) the extraction of data-driven biomarkers applying machine-learning techniques. A novel method for a unified and automated estimation of structural volumes and volume changes is proposed. Furthermore, a new technique that allows the low-dimensional representation of a high-dimensional image population for data analysis and visualization is described. All presented methods are evaluated on images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), providing a large and diverse clinical database. A rigorous evaluation of the power of all identified biomarkers to discriminate between clinical subject groups is presented. In addition, the agreement of automatically derived volumes with reference labels as well as the power of the proposed method to measure changes in a subject's atrophy rate are assessed. The proposed methods compare favorably to state-of-the art techniques in neuroimaging in terms of accuracy, robustness and run-time

Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections

Nonlinear registration of 2D histological sections with corresponding slices of MRI data is a critical step of 3D histology reconstruction. This task is difficult due to the large differences in image contrast and resolution, as well as the complex nonrigid distortions produced when sectioning the sample and mounting it on the glass slide. It has been shown in brain MRI registration that better spatial alignment across modalities can be obtained by synthesizing one modality from the other and then using intra-modality registration metrics, rather than by using mutual information (MI) as metric. However, such an approach typically requires a database of aligned images from the two modalities, which is very difficult to obtain for histology/MRI. Here, we overcome this limitation with a probabilistic method that simultaneously solves for registration and synthesis directly on the target images, without any training data. In our model, the MRI slice is assumed to be a contrast-warped, spatially deformed version of the histological section. We use approximate Bayesian inference to iteratively refine the probabilistic estimate of the synthesis and the registration, while accounting for each other's uncertainty. Moreover, manually placed landmarks can be seamlessly integrated in the framework for increased performance. Experiments on a synthetic dataset show that, compared with MI, the proposed method makes it possible to use a much more flexible deformation model in the registration to improve its accuracy, without compromising robustness. Moreover, our framework also exploits information in manually placed landmarks more efficiently than MI, since landmarks inform both synthesis and registration - as opposed to registration alone. Finally, we show qualitative results on the public Allen atlas, in which the proposed method provides a clear improvement over MI based registration

Automated Atlas-Based Segmentation of Brain Structures in MR Images: Application to a Population-Based Imaging Study

The final type of segmentationmethod is atlas-based segmentation (sometimes also called label propagation). In this approach, additional knowledge is introduced through an atlas image, in which an expert has labeled the brain structures of interest. The atlas is first registered to the target image, and the resulting transformation is then used to deform the atlas labels to the coordinate system of the target image. During registration the similarity between the warped atlas image and the target image is maximized, while at the same time the deformation is constrained to ensure that the spatial information of the atlas is maintained

Quantitation in MRI : application to ageing and epilepsy

Multi-atlas propagation and label fusion techniques have recently been developed for segmenting the human brain into multiple anatomical regions. In this thesis, I investigate possible adaptations of these current state-of-the-art methods. The aim is to study ageing on the one hand, and on the other hand temporal lobe epilepsy as an example for a neurological disease. Overall effects are a confounding factor in such anatomical analyses. Intracranial volume (ICV) is often preferred to normalize for global effects as it allows to normalize for estimated maximum brain size and is hence independent of global brain volume loss, as seen in ageing and disease. I describe systematic differences in ICV measures obtained at 1.5T versus 3T, and present an automated method of measuring intracranial volume, Reverse MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space. I show that this is comparable to manual measurements and robust against field strength differences. Correct and robust segmentation of target brains which show gross abnormalities, such as ventriculomegaly, is important for the study of ageing and disease. We achieved this with incorporating tissue classification information into the image registration process. The best results in elderly subjects, patients with TLE and healthy controls were achieved using a new approach using multi-atlas propagation with enhanced registration (MAPER). I then applied MAPER to the problem of automatically distinguishing patients with TLE with (TLE-HA) and without (TLE-N) hippocampal atrophy on MRI from controls, and determine the side of seizure onset. MAPER-derived structural volumes were used for a classification step consisting of selecting a set of discriminatory structures and applying support vector machine on the structural volumes as well as morphological similarity information such as volume difference obtained with spectral analysis. Acccuracies were 91-100 %, indicating that the method might be clinically useful. Finally, I used the methods developed in the previous chapters to investigate brain regional volume changes across the human lifespan in over 500 healthy subjects between 20 to 90 years of age, using data from three different scanners (2x 1.5T, 1x 3T), using the IXI database. We were able to confirm several known changes, indicating the veracity of the method. In addition, we describe the first multi-region, whole-brain database of normal ageing

Combining Shape and Learning for Medical Image Analysis

Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. They should produce morphologically and physiologically plausible results while generalizing well to unseen and rare anatomies. Still, there are few, if any, applications where today\u27s automatic methods succeed to meet these requirements.\ua0The focus of this thesis is two tasks essential for enabling automatic medical image assessment, medical image segmentation and medical image registration. Medical image registration, i.e. aligning two separate medical images, is used as an important sub-routine in many image analysis tools as well as in image fusion, disease progress tracking and population statistics. Medical image segmentation, i.e. delineating anatomically or physiologically meaningful boundaries, is used for both diagnostic and visualization purposes in a wide range of applications, e.g. in computer-aided diagnosis and surgery.The thesis comprises five papers addressing medical image registration and/or segmentation for a diverse set of applications and modalities, i.e. pericardium segmentation in cardiac CTA, brain region parcellation in MRI, multi-organ segmentation in CT, heart ventricle segmentation in cardiac ultrasound and tau PET registration. The five papers propose competitive registration and segmentation methods enabled by machine learning techniques, e.g. random decision forests and convolutional neural networks, as well as by shape modelling, e.g. multi-atlas segmentation and conditional random fields

Ultrasound imaging system combined with multi-modality image analysis algorithms to monitor changes in anatomical structures

This dissertation concerns the development and validation of an ultrasound imaging system and novel image analysis algorithms applicable to multiple imaging modalities. The ultrasound imaging system will include a framework for 3D volume reconstruction of freehand ultrasound: a mechanism to register the 3D volumes across time and subjects, as well as with other imaging modalities, and a playback mechanism to view image slices concurrently from different acquisitions that correspond to the same anatomical region. The novel image analysis algorithms include a noise reduction method that clusters pixels into homogenous patches using a directed graph of edges between neighboring pixels, a segmentation method that creates a hierarchical graph structure using statistical analysis and a voting system to determine the similarity between homogeneous patches given their neighborhood, and finally, a hybrid atlas-based registration method that makes use of intensity corrections induced at anatomical landmarks to regulate deformable registration. The combination of the ultrasound imaging system and the image analysis algorithms will provide the ability to monitor nerve regeneration in patients undergoing regenerative, repair or transplant strategies in a sequential, non-invasive manner, including visualization of registered real-time and pre-acquired data, thus enabling preventive and therapeutic strategies for nerve regeneration in Composite Tissue Allotransplantation (CTA). The registration algorithm is also applied to MR images of the brain to obtain reliable and efficient segmentation of the hippocampus, which is a prominent structure in the study of diseases of the elderly such as vascular dementia, Alzheimer’s, and late life depression. Experimental results on 2D and 3D images, including simulated and real images, with illustrations visualizing the intermediate outcomes and the final results are presented.

Generative-Discriminative Low Rank Decomposition for Medical Imaging Applications

In this thesis, we propose a method that can be used to extract biomarkers from medical images toward early diagnosis of abnormalities. Surge of demand for biomarkers and availability of medical images in the recent years call for accurate, repeatable, and interpretable approaches for extracting meaningful imaging features. However, extracting such information from medical images is a challenging task because the number of pixels (voxels) in a typical image is in order of millions while even a large sample-size in medical image dataset does not usually exceed a few hundred. Nevertheless, depending on the nature of an abnormality, only a parsimonious subset of voxels is typically relevant to the disease; therefore various notions of sparsity are exploited in this thesis to improve the generalization performance of the prediction task. We propose a novel discriminative dimensionality reduction method that yields good classification performance on various datasets without compromising the clinical interpretability of the results. This is achieved by combining the modelling strength of generative learning framework and the classification performance of discriminative learning paradigm. Clinical interpretability can be viewed as an additional measure of evaluation and is also helpful in designing methods that account for the clinical prior such as association of certain areas in a brain to a particular cognitive task or connectivity of some brain regions via neural fibres. We formulate our method as a large-scale optimization problem to solve a constrained matrix factorization. Finding an optimal solution of the large-scale matrix factorization renders off-the-shelf solver computationally prohibitive; therefore, we designed an efficient algorithm based on the proximal method to address the computational bottle-neck of the optimization problem. Our formulation is readily extended for different scenarios such as cases where a large cohort of subjects has uncertain or no class labels (semi-supervised learning) or a case where each subject has a battery of imaging channels (multi-channel), \etc. We show that by using various notions of sparsity as feasible sets of the optimization problem, we can encode different forms of prior knowledge ranging from brain parcellation to brain connectivity

Contributions of Continuous Max-Flow Theory to Medical Image Processing

Discrete graph cuts and continuous max-flow theory have created a paradigm shift in many areas of medical image processing. As previous methods limited themselves to analytically solvable optimization problems or guaranteed only local optimizability to increasingly complex and non-convex functionals, current methods based now rely on describing an optimization problem in a series of general yet simple functionals with a global, but non-analytic, solution algorithms. This has been increasingly spurred on by the availability of these general-purpose algorithms in an open-source context. Thus, graph-cuts and max-flow have changed every aspect of medical image processing from reconstruction to enhancement to segmentation and registration. To wax philosophical, continuous max-flow theory in particular has the potential to bring a high degree of mathematical elegance to the field, bridging the conceptual gap between the discrete and continuous domains in which we describe different imaging problems, properties and processes. In Chapter 1, we use the notion of infinitely dense and infinitely densely connected graphs to transfer between the discrete and continuous domains, which has a certain sense of mathematical pedantry to it, but the resulting variational energy equations have a sense of elegance and charm. As any application of the principle of duality, the variational equations have an enigmatic side that can only be decoded with time and patience. The goal of this thesis is to show the contributions of max-flow theory through image enhancement and segmentation, increasing incorporation of topological considerations and increasing the role played by user knowledge and interactivity. These methods will be rigorously grounded in calculus of variations, guaranteeing fuzzy optimality and providing multiple solution approaches to addressing each individual problem