13 research outputs found

    Deep Networks Based Energy Models for Object Recognition from Multimodality Images

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    Object recognition has been extensively investigated in computer vision area, since it is a fundamental and essential technique in many important applications, such as robotics, auto-driving, automated manufacturing, and security surveillance. According to the selection criteria, object recognition mechanisms can be broadly categorized into object proposal and classification, eye fixation prediction and saliency object detection. Object proposal tends to capture all potential objects from natural images, and then classify them into predefined groups for image description and interpretation. For a given natural image, human perception is normally attracted to the most visually important regions/objects. Therefore, eye fixation prediction attempts to localize some interesting points or small regions according to human visual system (HVS). Based on these interesting points and small regions, saliency object detection algorithms propagate the important extracted information to achieve a refined segmentation of the whole salient objects. In addition to natural images, object recognition also plays a critical role in clinical practice. The informative insights of anatomy and function of human body obtained from multimodality biomedical images such as magnetic resonance imaging (MRI), transrectal ultrasound (TRUS), computed tomography (CT) and positron emission tomography (PET) facilitate the precision medicine. Automated object recognition from biomedical images empowers the non-invasive diagnosis and treatments via automated tissue segmentation, tumor detection and cancer staging. The conventional recognition methods normally utilize handcrafted features (such as oriented gradients, curvature, Haar features, Haralick texture features, Laws energy features, etc.) depending on the image modalities and object characteristics. It is challenging to have a general model for object recognition. Superior to handcrafted features, deep neural networks (DNN) can extract self-adaptive features corresponding with specific task, hence can be employed for general object recognition models. These DNN-features are adjusted semantically and cognitively by over tens of millions parameters corresponding to the mechanism of human brain, therefore leads to more accurate and robust results. Motivated by it, in this thesis, we proposed DNN-based energy models to recognize object on multimodality images. For the aim of object recognition, the major contributions of this thesis can be summarized below: 1. We firstly proposed a new comprehensive autoencoder model to recognize the position and shape of prostate from magnetic resonance images. Different from the most autoencoder-based methods, we focused on positive samples to train the model in which the extracted features all come from prostate. After that, an image energy minimization scheme was applied to further improve the recognition accuracy. The proposed model was compared with three classic classifiers (i.e. support vector machine with radial basis function kernel, random forest, and naive Bayes), and demonstrated significant superiority for prostate recognition on magnetic resonance images. We further extended the proposed autoencoder model for saliency object detection on natural images, and the experimental validation proved the accurate and robust saliency object detection results of our model. 2. A general multi-contexts combined deep neural networks (MCDN) model was then proposed for object recognition from natural images and biomedical images. Under one uniform framework, our model was performed in multi-scale manner. Our model was applied for saliency object detection from natural images as well as prostate recognition from magnetic resonance images. Our experimental validation demonstrated that the proposed model was competitive to current state-of-the-art methods. 3. We designed a novel saliency image energy to finely segment salient objects on basis of our MCDN model. The region priors were taken into account in the energy function to avoid trivial errors. Our method outperformed state-of-the-art algorithms on five benchmarking datasets. In the experiments, we also demonstrated that our proposed saliency image energy can boost the results of other conventional saliency detection methods

    Segmentation of pelvic structures from preoperative images for surgical planning and guidance

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    Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

    The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge, part II

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    This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study

    Scientific Advances in STEM: From Professor to Students

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    This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields

    Bioinspired metaheuristic algorithms for global optimization

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    This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

    Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

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    In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

    Libro de actas. XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica

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    596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio científico de conocimiento, experiencias y promoción de la I D i en Ingeniería Biomédica. Un punto de encuentro de científicos, profesionales de la industria, ingenieros biomédicos y profesionales clínicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clínica de la ingeniería biomédica. En la presente edición, más de 160 trabajos de alto nivel científico serán presentados en áreas relevantes de la ingeniería biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales. Cabe destacar las sesiones dedicadas a la competición por el Premio José María Ferrero Corral, y la sesión de competición de alumnos de Grado en Ingeniería biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores

    A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

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    When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

    A Statistical Approach to the Alignment of fMRI Data

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    Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods
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