Functional and structural MRI image analysis for brain glial tumors treatment

Cotutela con il Dipartimento di Biotecnologie e Scienze della Vita, Universiità degli Studi dell'Insubria.openThis Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient.openInformaticaPedoia, ValentinaPedoia, Valentin

Functional and structural MRI image analysis for brain glial tumors treatment

This Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient

A perceptual learning model to discover the hierarchical latent structure of image collections

Biology has been an unparalleled source of inspiration for the work of researchers in several scientific and engineering fields including computer vision. The starting point of this thesis is the neurophysiological properties of the human early visual system, in particular, the cortical mechanism that mediates learning by exploiting information about stimuli repetition. Repetition has long been considered a fundamental correlate of skill acquisition andmemory formation in biological aswell as computational learning models. However, recent studies have shown that biological neural networks have differentways of exploiting repetition in forming memory maps. The thesis focuses on a perceptual learning mechanism called repetition suppression, which exploits the temporal distribution of neural activations to drive an efficient neural allocation for a set of stimuli. This explores the neurophysiological hypothesis that repetition suppression serves as an unsupervised perceptual learning mechanism that can drive efficient memory formation by reducing the overall size of stimuli representation while strengthening the responses of the most selective neurons. This interpretation of repetition is different from its traditional role in computational learning models mainly to induce convergence and reach training stability, without using this information to provide focus for the neural representations of the data. The first part of the thesis introduces a novel computational model with repetition suppression, which forms an unsupervised competitive systemtermed CoRe, for Competitive Repetition-suppression learning. The model is applied to generalproblems in the fields of computational intelligence and machine learning. Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data. In particular, it is shown that the repetition suppression mechanism efficiently addresses the issues of automatically estimating the number of clusters within the data, as well as filtering noise and irrelevant input components in highly dimensional data, e.g. gene expression levels from DNA Microarrays. The CoRe model produces relevance estimates for the each covariate which is useful, for instance, to discover the best discriminating bio-markers. The description of the model includes a theoretical analysis using Huber’s robust statistics to show that the model is robust to outliers and noise in the data. The convergence properties of themodel also studied. It is shown that, besides its biological underpinning, the CoRe model has useful properties in terms of asymptotic behavior. By exploiting a kernel-based formulation for the CoRe learning error, a theoretically sound motivation is provided for the model’s ability to avoid local minima of its loss function. To do this a necessary and sufficient condition for global error minimization in vector quantization is generalized by extending it to distance metrics in generic Hilbert spaces. This leads to the derivation of a family of kernel-based algorithms that address the local minima issue of unsupervised vector quantization in a principled way. The experimental results show that the algorithm can achieve a consistent performance gain compared with state-of-the-art learning vector quantizers, while retaining a lower computational complexity (linear with respect to the dataset size). Bridging the gap between the low level representation of the visual content and the underlying high-level semantics is a major research issue of current interest. The second part of the thesis focuses on this problem by introducing a hierarchical and multi-resolution approach to visual content understanding. On a spatial level, CoRe learning is used to pool together the local visual patches by organizing them into perceptually meaningful intermediate structures. On the semantical level, it provides an extension of the probabilistic Latent Semantic Analysis (pLSA) model that allows discovery and organization of the visual topics into a hierarchy of aspects. The proposed hierarchical pLSA model is shown to effectively address the unsupervised discovery of relevant visual classes from pictorial collections, at the same time learning to segment the image regions containing the discovered classes. Furthermore, by drawing on a recent pLSA-based image annotation system, the hierarchical pLSA model is extended to process and representmulti-modal collections comprising textual and visual data. The results of the experimental evaluation show that the proposed model learns to attach textual labels (available only at the level of the whole image) to the discovered image regions, while increasing the precision/ recall performance with respect to flat, pLSA annotation model

Region-based spatial and temporal image segmentation

This work discusses region-based representations for image and video sequence segmentation. It presents effective image segmentation techniques and demonstrates how these techniques may be integrated into algorithms that solve some of the motion segmentation problems. The region-based representation offers a way to perform a first level of abstraction and to reduce the number of elements to process with respect to the classical pixel-based representation. Motion segmentation is a fundamental technique for the analysis and the understanding of image sequences of real scenes. Motion segmentation 'describes' the sequence as sets of pixels moving coherently across one sequence with associated motions. This description is essential to the identification of the objects in the scene and to a more efficient manipulation of video sequences. This thesis presents a hybrid framework based on the combination of spatial and motion information for the segmentation of moving objects in image sequences accordingly with their motion. We formulate the problem as graph labelling over a region moving graph where nodes correspond coherently to moving atomic regions. This is a flexible high-level representation which individualizes moving independent objects. Starting from an over-segmentation of the image, the objects are formed by merging neighbouring regions together based on their mutual spatial and temporal similarity, taking spatial and motion information into account with the emphasis being on the second. Final segmentation is obtained by a spectral-based graph cuts approach. The initial phase for the moving object segmentation aims to reduce image noise without destroying the topological structure of the objects by anisotropic bilateral filtering. An initial spatial partition into a set of homogeneous regions is obtained by the watershed transform. Motion vector of each region is estimated by a variational approach. Next a region moving graph is constructed by a combination of normalized similarity between regions where mean intensity of the regions, gradient magnitude between regions, and motion information of the regions are considered. The motion similarity measure among regions is based on human perceptual characteristics. Finally, a spectral-based graph cut approach clusters and labels each moving region. The motion segmentation approach is based on a static image segmentation method proposed by the author of this dissertation. The main idea is to use atomic regions to guide a segmentation using the intensity and the gradient information through a similarity graph-based approach. This method produces simpler segmentations, less over-segmented and compares favourably with the state-of-the-art methods. To evaluate the segmentation results a new evaluation metric is proposed, which takes into attention the way humans perceive visual information. By incorporating spatial and motion information simultaneously in a region-based framework, we can visually obtain meaningful segmentation results. Experimental results of the proposed technique performance are given for different image sequences with or without camera motion and for still images. In the last case a comparison with the state-of-the-art approaches is made

Contributions to improve the technologies supporting unmanned aircraft operations

Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

DEVELOPING INTEGRATED MACHINE LEARNING MODELS FOR AUTOMATIC COMPUTER-AIDED DIAGNOSIS IN ISCHEMIC ACUTE STROKE MRI

Fast detection and quantification of lesion cores in diffusion weighted images (DWIs) has been highly anticipated in clinical and research communities for planning treatment of acute stroke. The recent emergence of successful machine learning (ML) methods, especially Deep Learning (DL), enables automatic Computer Aided Diagnosis (CAD) of stroke in DWIs. However, the lack of publicly available large-scale data and ML models in clinical acute stroke DWI application are still the bottlenecks. In this work, we established the first large annotated open-source database of 2,888 clinical acute stroke MRIs (Chapter 2) to train and develop ML models for automatic stroke lesion detection and segmentation in clinical acute stroke MRI (Chapter 3). For automatic measurement of infarcted arterial territories, the first digital 3D deformable brain arterial territory atlas was created (Chapter 4). In addition, a fully automatic ML system is created to generate automatic radiological reports (Chapter 5 and 6) for calculation of ASPECTS, prediction and quantification of infarcted arterial and anatomical regions, and estimation of hydrocephalus presented in acute stroke MRI. The complete ML system in this work runs locally in real time with minimal computational requirements. It is publicly available and readily useful for non-expert users