Precision Imaging: more descriptive, predictive and integrative imaging

Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological modelbased imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field

Three-dimensional model-based analysis of vascular and cardiac images

This thesis is concerned with the geometrical modeling of organs to perform medical image analysis tasks. The thesis is divided in two main parts devoted to model linear vessel segments and the left ventricle of the heart, respectively. Chapters 2 to 4 present different aspects of a model-based technique for semi-automated quantification of linear vessel segments from 3-D Magnetic Resonance Angiography (MRA). Chapter 2 is concerned with a multiscale filter for the enhancement of vessels in 2-D and 3-D angiograms. Chapter 3 applies the filter developed in Chapter 2 to determine the central vessel axis in 3-D MRA images. This procedure is initialized using an efficient user interaction technique that naturally incorporates the knowledge of the operator about the vessel of interest. Also in this chapter, a linear vessel model is used to recover the position of the vessel wall in order to carry out an accurate quantitative analysis of vascular morphology. Prior knowledge is provided in two main forms: a cylindrical model introduces a shape prior while prior knowledge on the image acquisition (type of MRA technique) is used to define an appropriate vessel boundary criterion. In Chapter 4 an extensive in vitro and in vivo evaluation of the algorithm introduced in Chapter 3 is described. Chapters 5 to 7 change the focus to 3D cardiac image analysis from Magnetic Resonance Imaging. Chapter 5 presents an extensive survey, a categorization and a critical review of the field of cardiac modeling. Chapter 6 and Chapter 7 present successive refinements of a method for building statistical models of shape variability with particular emphasis on cardiac modeling. The method is based on an elastic registration method using hierarchical free-form deformations. A 3D shape model of the left and right ventricles of the heart was constructed. This model contains both the average shape of these organs as well as their shape variability. The methodology presented in the last two chapters could also be applied to other anatomical structures. This has been illustrated in Chapter 6 with examples of geometrical models of the nucleus caudate and the radius

Multi-class Image Segmentation in Fluorescence Microscopy Using Polytrees

Multi-class segmentation is a crucial step in cell image analysis. This process becomes challenging when little information is available for recognising cells from the background, due to their poor discriminative features. To alleviate this, directed acyclic graphs such as trees have been proposed to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, modelling the relations between labels of multiple classes becomes difficult. To overcome this limitation, we propose a polytree graphical model that captures label proximity relations more naturally compared to tree based approaches. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on the polytree. The algorithm is evaluated using simulated data, synthetic images and real fluorescence microscopy images. Our method achieves Dice scores of 94.5% and 98% on macrophage and seed classes, respectively, outperforming GMM based classifiers

Virtual endovascular treatment of intracranial aneurysms: models and uncertainty

Virtual endovascular treatment models (VETMs) have been developed with the view to aid interventional neuroradiologists and neurosurgeons to pre-operatively analyze the comparative efficacy and safety of endovascular treatments for intracranial aneurysms. Based on the current state of VETMs in aneurysm rupture risk stratification and in patient-specific prediction of treatment outcomes, we argue there is a need to go beyond personalized biomechanical flow modeling assuming deterministic parameters and error-free measurements. The mechanobiological effects associated with blood clot formation are important factors in therapeutic decision making and models of post-treatment intra-aneurysmal biology and biochemistry should be linked to the purely hemodynamic models to improve the predictive power of current VETMs. The influence of model and parameter uncertainties associated to each component of a VETM is, where feasible, quantified via a random-effects meta-analysis of the literature. This allows estimating the pooled effect size of these uncertainties on aneurysmal wall shear stress. From such meta-analyses, two main sources of uncertainty emerge where research efforts have so far been limited: (1) vascular wall distensibility, and (2) intra/intersubject systemic flow variations. In the future, we suggest that current deterministic computational simulations need to be extended with strategies for uncertainty mitigation, uncertainty exploration, and sensitivity reduction techniques. WIREs Syst Biol Med 2017, 9:e1385. doi: 10.1002/wsbm.138

Vascular dysfunction in the pathogenesis of Alzheimer's disease - A review of endothelium-mediated mechanisms and ensuing vicious circles

Late-onset dementia is a major health concern in the ageing population. Alzheimer's disease (AD) accounts for the largest proportion (65-70%) of dementia cases in the older population. Despite considerable research effort, the pathogenesis of late-onset AD remains unclear. Substantial evidence suggests that the neurodegenerative process is initiated by chronic cerebral hypoperfusion (CCH) caused by ageing and cardiovascular conditions. CCH causes reduced oxygen, glucose and other nutrient supply to the brain, with direct damage not only to the parenchymal cells, but also to the blood-brain barrier (BBB), a key mediator of cerebral homeostasis. BBB dysfunction mediates the indirect neurotoxic effects of CCH by promoting oxidative stress, inflammation, paracellular permeability, and dysregulation of nitric oxide, a key regulator of regional blood flow. As such, BBB dysfunction mediates a vicious circle in which cerebral perfusion is reduced further and the neurodegenerative process is accelerated. Endothelial interaction with pericytes and astrocytes could also play a role in the process. Reciprocal interactions between vascular dysfunction and neurodegeneration could further contribute to the development of the disease.A comprehensive overview of the complex scenario of interacting endothelium-mediated processes is currently lacking, and could prospectively contribute to the identification of adequate therapeutic interventions. This study reviews the current literature of in vitro and ex vivo studies on endothelium-mediated mechanisms underlying vascular dysfunction in AD pathogenesis, with the aim of presenting a comprehensive overview of the complex network of causative relationships. Particular emphasis is given to vicious circles which can accelerate the process of neurovascular degeneration

Accurate and Efficient Linear Structure Segmentation by Leveraging Ad Hoc Features with Learned Filters

Extracting linear structures, such as blood vessels or dendrites, from images is crucial in many medical imagery applications, and many handcrafted features have been proposed to solve this problem. However, such features rely on assumptions that are never entirely true. Learned features, on the other hand, can capture image characteristics difficult to define analytically, but tend to be much slower to compute than handcrafted features. We propose to complement handcrafted methods with features found using very recent Machine Learning techniques, and we show that even few filters are sufficient to efficiently leverage handcrafted features. We demonstrate our approach on the STARE, DRIVE, and BF2D datasets, and on 2D projections of neural images from the DIADEM challenge. Our proposal outperforms handcrafted methods, and pairs up with learning-only approaches at a fraction of their computational cost

Left-ventricular epi- and endocardium extraction from 3D ultrasound images using an automatically constructed 3D ASM

© 2014 Taylor & Francis.In this paper, we propose an automatic method for constructing an active shape model (ASM) to segment the complete cardiac left ventricle in 3D ultrasound (3DUS) images, which avoids costly manual landmarking. The automatic construction of the ASM has already been addressed in the literature; however, the direct application of these methods to 3DUS is hampered by a high level of noise and artefacts. Therefore, we propose to construct the ASM by fusing the multidetector computed tomography data, to learn the shape, with the artificially generated 3DUS, in order to learn the neighbourhood of the boundaries. Our artificial images were generated by two approaches: a faster one that does not take into account the geometry of the transducer, and a more comprehensive one, implemented in Field II toolbox. The segmentation accuracy of our ASM was evaluated on 20 patients with left-ventricular asynchrony, demonstrating plausibility of the approach

Local volume fraction distributions of axons, astrocytes, and myelin in deep subcortical white matter

This study aims to statistically describe histologically stained white matter brain sections to subsequently inform and validate diffusion MRI techniques. For the first time, we characterise volume fraction distributions of three of the main structures in deep subcortical white matter (axons, astrocytes, and myelinated axons) in a representative cohort of an ageing population for which well-characterized neuropathology data is available. We analysed a set of samples from 90 subjects of the Cognitive Function and Ageing Study (CFAS), stratified into three groups of 30 subjects each, in relation to the presence of age-associated deep subcortical lesions. This provides volume fraction distributions in different scenarios relevant to brain diffusion MRI in dementia. We also assess statistically significant differences found between these groups. In agreement with previous literature, our results indicate that white matter lesions are related with a decrease in the myelinated axons fraction and an increase in astrocytic fraction, while no statistically significant changes occur in axonal mean fraction. In addition, we introduced a framework to quantify volume fraction distributions from 2D immunohistochemistry images, which is validated against in silico simulations. Since a trade-off between precision and resolution emerged, we also performed an assessment of the optimal scale for computing such distributions