Geometric Feature Extraction by a Multi-Marked Point Process

International audienceThis paper presents a new stochastic marked point process for describing images in terms of a finite library of geometric objects. Image analysis based on conventional marked point processes has already produced convincing results but at the expense of parameter tuning, computing time, and model specificity. Our more general multimarked point process has simpler parametric setting, yields notably shorter computing times, and can be applied to a variety of applications. Both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model, and a Jump-Diffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show that the proposed approach has good potential. We conclude with a discussion about the insertion of more complex object interactions in the model by studying the compromise between model complexity and efficiency

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

Computational fluid dynamics indicators to improve cardiovascular pathologies

In recent years, the study of computational hemodynamics within anatomically complex vascular regions has generated great interest among clinicians. The progress in computational fluid dynamics, image processing and high-performance computing haveallowed us to identify the candidate vascular regions for the appearance of cardiovascular diseases and to predict how this disease may evolve. Medicine currently uses a paradigm called diagnosis. In this thesis we attempt to introduce into medicine the predictive paradigm that has been used in engineering for many years. The objective of this thesis is therefore to develop predictive models based on diagnostic indicators for cardiovascular pathologies. We try to predict the evolution of aortic abdominal aneurysm, aortic coarctation and coronary artery disease in a personalized way for each patient. To understand how the cardiovascular pathology will evolve and when it will become a health risk, it is necessary to develop new technologies by merging medical imaging and computational science. We propose diagnostic indicators that can improve the diagnosis and predict the evolution of the disease more efficiently than the methods used until now. In particular, a new methodology for computing diagnostic indicators based on computational hemodynamics and medical imaging is proposed. We have worked with data of anonymous patients to create real predictive technology that will allow us to continue advancing in personalized medicine and generate more sustainable health systems. However, our final aim is to achieve an impact at a clinical level. Several groups have tried to create predictive models for cardiovascular pathologies, but they have not yet begun to use them in clinical practice. Our objective is to go further and obtain predictive variables to be used practically in the clinical field. It is to be hoped that in the future extremely precise databases of all of our anatomy and physiology will be available to doctors. These data can be used for predictive models to improve diagnosis or to improve therapies or personalized treatments.En els últims anys, l'estudi de l'hemodinàmica computacional en regions vasculars anatòmicament complexes ha generat un gran interès entre els clínics. El progrés obtingut en la dinàmica de fluids computacional, en el processament d'imatges i en la computació d'alt rendiment ha permès identificar regions vasculars on poden aparèixer malalties cardiovasculars, així com predir-ne l'evolució. Actualment, la medicina utilitza un paradigma anomenat diagnòstic. En aquesta tesi s'intenta introduir en la medicina el paradigma predictiu utilitzat des de fa molts anys en l'enginyeria. Per tant, aquesta tesi té com a objectiu desenvolupar models predictius basats en indicadors de diagnòstic de patologies cardiovasculars. Tractem de predir l'evolució de l'aneurisma d'aorta abdominal, la coartació aòrtica i la malaltia coronària de forma personalitzada per a cada pacient. Per entendre com la patologia cardiovascular evolucionarà i quan suposarà un risc per a la salut, cal desenvolupar noves tecnologies mitjançant la combinació de les imatges mèdiques i la ciència computacional. Proposem uns indicadors que poden millorar el diagnòstic i predir l'evolució de la malaltia de manera més eficient que els mètodes utilitzats fins ara. En particular, es proposa una nova metodologia per al càlcul dels indicadors de diagnòstic basada en l'hemodinàmica computacional i les imatges mèdiques. Hem treballat amb dades de pacients anònims per crear una tecnologia predictiva real que ens permetrà seguir avançant en la medicina personalitzada i generar sistemes de salut més sostenibles. Però el nostre objectiu final és aconseguir un impacte en l¿àmbit clínic. Diversos grups han tractat de crear models predictius per a les patologies cardiovasculars, però encara no han començat a utilitzar-les en la pràctica clínica. El nostre objectiu és anar més enllà i obtenir variables predictives que es puguin utilitzar de forma pràctica en el camp clínic. Es pot preveure que en el futur tots els metges disposaran de bases de dades molt precises de tota la nostra anatomia i fisiologia. Aquestes dades es poden utilitzar en els models predictius per millorar el diagnòstic o per millorar teràpies o tractaments personalitzats.Postprint (published version

Maximum entropy models in the analysis of genome-wide data in cancer research

This thesis studies the maximum entropy principle in statistical modelling. Applications are taken from the emerging field of cancer genomics. We start with a short introduction to the biology of cancer in chapter 1. In chapter 2, we discuss general principles of statistical modelling. We discuss in detail the maximum entropy principle in statistical modelling. In particular, we show that many statistical models can be put in a unified framework based on the principle of maximum entropy, which maps them into problems of statistical mechanics. In chapter 3, we consider a particular maximum entropy model, the Ising model, in the context of the inverse Ising problem. We introduce a Bethe–Peierls approximation to the inverse Ising problem. We then also suggest a modification for the mean-field approximation to work at low temperatures. The following chapters apply maximum entropy models to different problems of cancer genomics. A direct application of the inverse Ising problem to gene copy-number data of cancer cells is described in chapter 4. In chapter 5, we extend the concepts of indirect correlations and direct couplings of the inverse Ising problem to investigate the influence of gene copy-numbers on gene expressions in cancer cells. We show that the correlations in gene expression need not be due to regulatory interactions between genes. Instead, correlations in gene expression of cancer cells can be induced by the correlations in their copy-numbers, which is due to the geometrical organisation of the genome. We show that a simple maximum entropy-model can disentangle copy-number-induced correlations and the so-called “bare-correlations” in gene expression, which capture the effect of regulatory interactions alone. Chapter 6 is devoted to cancer classification. We introduce a simple semi-supervised learning algorithm to train a mixture of paramagnetic models with Ising spins to classify cancer mutation profiles. We show that, with the capability of both learning from unlabelled samples and correcting mislabelled samples, this learning algorithm outperforms both the supervised and unsupervised learning algorithms. The two appendices A and B summarise recent studies on sensitivity and resistance of cancer cells to therapy. The results of chapter 3 were published in H. C. Nguyen and J. Berg (2012a). “Bethe– Peierls approximation and the inverse Ising problem”. J. Stat. Mech. P03004; and H. C. Nguyen and J. Berg (2012b). “Mean-field theory for the inverse Ising problem at low temperatures”. Phys. Rev. Lett. 109, p. 50602. Some results of chapter 6 were published as a part of The Clinical Lung Cancer Genome Project (CLCGP) and Network Genomic Medicine (NGM) (2013). “A genomics-based classification of human lung tumors”. Science Transl. Med. 5.209, 209ra153

Cognitive Biology: Dealing with Information from Bacteria to Minds

Providing a new conceptual scaffold for further research in biology and cognition, this text introduces the new field of cognitive biology, treating developing organisms as information processors which use cognition to control and modify their environments

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 376)

This bibliography lists 265 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jun. 1993. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance