Facial Point Detection using Boosted Regression and Graph Models

Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point’s location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors

Accurate and reliable segmentation of the optic disc in digital fundus images

We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE)

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

Blood flow dynamics in surviving patients with repaired Tetralogy of Fallot

Tetralogy of Fallot (TOF) is a congenital heart disease that causes structural abnormalities in the pulmonary arteries, which in turn disrupt the blood flow. Surgical repair is necessary early in childhood, but chronic complications are common in the adult surviving patients. Pulmonary valve replacement is an operation performed in the repaired TOF (rTOF) patients to overcome the right ventricular overload, but the optimal timing remains a challenge. The main research question is whether the haemodynamic environment of the pulmonary junction can clarify the interplay between the upstream and downstream pulmonary vasculature. Therefore, an extensive analysis of the effect of morphological and flow characteristics in healthy and rTOF models was performed, under various boundary conditions (BCs). The effects of branch angle and origin, branch stenosis, flow splits and pulmonary resistance were investigated in idealised two-dimensional geometries, representative of healthy and rTOF cases, explaining the elevated pressure in the LPA, and clearly showing that downstream pressure and peripheral resistance alter the flow development and the flow split between the two daughter branches. Various modelling parameters were also tested, demonstrating the importance of the valve, and how it disturbs the flow patterns along the MPA. The elasticity of arterial wall had a minimal effect on the flow development while the WSS deviated based on the rheological model assumed. Finally, anatomically realistic three-dimensional models of rTOF patients and healthy volunteers were reconstructed and morphological and flow features were analysed. Higher curvature and tortuosity were correlated with more complex secondary flow patterns, and higher Reynolds and Dean numbers, with increased regions of time-averaged wall shear stress. More importantly, the importance of patient-specificity in the rTOF models, and the variability of the geometric and flow characteristics within the population was highlighted, contrary to the observations in the healthy models. The results of this work could help clinicians evaluate the haemodynamic environment in the rTOF population and potentially predict patients at higher risk, prior to the appearance of severe complications.Tetralogy of Fallot (TOF) is a congenital heart disease that causes structural abnormalities in the pulmonary arteries, which in turn disrupt the blood flow. Surgical repair is necessary early in childhood, but chronic complications are common in the adult surviving patients. Pulmonary valve replacement is an operation performed in the repaired TOF (rTOF) patients to overcome the right ventricular overload, but the optimal timing remains a challenge. The main research question is whether the haemodynamic environment of the pulmonary junction can clarify the interplay between the upstream and downstream pulmonary vasculature. Therefore, an extensive analysis of the effect of morphological and flow characteristics in healthy and rTOF models was performed, under various boundary conditions (BCs). The effects of branch angle and origin, branch stenosis, flow splits and pulmonary resistance were investigated in idealised two-dimensional geometries, representative of healthy and rTOF cases, explaining the elevated pressure in the LPA, and clearly showing that downstream pressure and peripheral resistance alter the flow development and the flow split between the two daughter branches. Various modelling parameters were also tested, demonstrating the importance of the valve, and how it disturbs the flow patterns along the MPA. The elasticity of arterial wall had a minimal effect on the flow development while the WSS deviated based on the rheological model assumed. Finally, anatomically realistic three-dimensional models of rTOF patients and healthy volunteers were reconstructed and morphological and flow features were analysed. Higher curvature and tortuosity were correlated with more complex secondary flow patterns, and higher Reynolds and Dean numbers, with increased regions of time-averaged wall shear stress. More importantly, the importance of patient-specificity in the rTOF models, and the variability of the geometric and flow characteristics within the population was highlighted, contrary to the observations in the healthy models. The results of this work could help clinicians evaluate the haemodynamic environment in the rTOF population and potentially predict patients at higher risk, prior to the appearance of severe complications

Active shape models with adaptive weights

Improvement of active shape models dealing with noisy and blured images of objects is developed. Results are tested on a set of radiographic images of welds. Improvement of performance of the proposed modified active shape models for radiographic images compared to conventional ones was shown basing on experimental results comparison.Удосконалено моделі активних форм для зашумлених зображень та зображень з нечіткими границями об'єктів. Результати протестовано на наборі рентгенографічних зображень зварних швів. Покращання функціонування вдосконалених моделей активних форм порівняно з класичними продемонстровано на порівнянні експериментальних результатів для рентгенографічних зображень

A retinal vasculature tracking system guided by a deep architecture

Many diseases such as diabetic retinopathy (DR) and cardiovascular diseases show their early signs on retinal vasculature. Analysing the vasculature in fundus images may provide a tool for ophthalmologists to diagnose eye-related diseases and to monitor their progression. These analyses may also facilitate the discovery of new relations between changes on retinal vasculature and the existence or progression of related diseases or to validate present relations. In this thesis, a data driven method, namely a Translational Deep Belief Net (a TDBN), is adapted to vasculature segmentation. The segmentation performance of the TDBN on low resolution images was found to be comparable to that of the best-performing methods. Later, this network is used for the implementation of super-resolution for the segmentation of high resolution images. This approach provided an acceleration during segmentation, which relates to down-sampling ratio of an input fundus image. Finally, the TDBN is extended for the generation of probability maps for the existence of vessel parts, namely vessel interior, centreline, boundary and crossing/bifurcation patterns in centrelines. These probability maps are used to guide a probabilistic vasculature tracking system. Although segmentation can provide vasculature existence in a fundus image, it does not give quantifiable measures for vasculature. The latter has more practical value in medical clinics. In the second half of the thesis, a retinal vasculature tracking system is presented. This system uses Particle Filters to describe vessel morphology and topology. Apart from previous studies, the guidance for tracking is provided with the combination of probability maps generated by the TDBN. The experiments on a publicly available dataset, REVIEW, showed that the consistency of vessel widths predicted by the proposed method was better than that obtained from observers. Moreover, very noisy and low contrast vessel boundaries, which were hardly identifiable to the naked eye, were accurately estimated by the proposed tracking system. Also, bifurcation/crossing locations during the course of tracking were detected almost completely. Considering these promising initial results, future work involves analysing the performance of the tracking system on automatic detection of complete vessel networks in fundus images.Open Acces

Characterization of Flow Dynamics in the Pulmonary Bifurcation of Patients With Repaired Tetralogy of Fallot: A Computational Approach

The hemodynamic environment of the pulmonary bifurcation is of great importance for adult patients with repaired tetralogy of Fallot (rTOF) due to possible complications in the pulmonary valve and narrowing of the left pulmonary artery (LPA). The aim of this study was to computationally investigate the effect of geometrical variability and flow split on blood flow characteristics in the pulmonary trunk of patient-specific models. Data from a cohort of seven patients was used retrospectively and the pulmonary hemodynamics was investigated using averaged and MRI-derived patient-specific boundary conditions on the individualized models, as well as a statistical mean geometry. Geometrical analysis showed that curvature and tortuosity are higher in the LPA branch, compared to the right pulmonary artery (RPA), resulting in complex flow patterns in the LPA. The computational analysis also demonstrated high time-averaged wall shear stress (TAWSS) at the outer wall of the LPA and the wall of the RPA proximal to the junction. Similar TAWSS patterns were observed for averaged boundary conditions, except for a significantly modified flow split assigned at the outlets. Overall, this study enhances our understanding about the flow development in the pulmonary bifurcation of rTOF patients and associates some morphological characteristics with hemodynamic parameters, highlighting the importance of patient-specificity in the models. To confirm these findings, further studies are required with a bigger cohort of patients

Maximization of Regional probabilities using Optimal Surface Graphs: Application to Carotid Artery Segmentation in MRI

__Purpose__ We present a segmentation method that maximizes regional probabilities enclosed by coupled surfaces using an Optimal Surface Graph (OSG) cut approach. This OSG cut determines the globally optimal solution given a graph constructed around an initial surface. While most methods for vessel wall segmentation only use edge information, we show that maximizing regional probabilities using an OSG improves the segmentation results. We applied this to automatically segment the vessel wall of the carotid artery in magnetic resonance images. __Methods__ First, voxel-wise regional probability maps were obtained using a Support Vector Machine classifier trained on local image features. Then the OSG segments the regions which maximizes the regional probabilities considering smoothness and topological constraints. __Results__ The method was evaluated on 49 carotid arteries from 30 subjects. The proposed method shows good accuracy with a Dice wall overlap of 74:1%+-4:3%, and significantly outperforms a published method based on an OSG using only surface information, the obtained segmentations using voxel-wise classification alone, and another published artery wall segmentation method based on a deformable surface model. Intra-class correlations (ICC) with manually measured lumen and wall volumes were similar to those obtained between observers. Finally, we show a good reproducibility of the method with ICC = 0:86 between the volumes measured in scans repeated within a short time interval. __Conclusions__ In this work a new segmentation method that uses both an OSG and regional probabilities is presented. The method shows good segmentations of the carotid artery in MRI and outperformed another segmentation method that uses OSG and edge information and the voxel-wise segmentation using the probability maps