Дисертації з теми "Intelligent imaging"
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Москаленко, Альона Сергіївна, Алена Сергеевна Москаленко, and Alona Serhiivna Moskalenko. "Intelligent decision support system for renal radionuclide imaging." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46806.
Fukuda, Toshio, Naoyuki Kubota, Baiqing Sun, Fei Chen, Tomoya Fukukawa, and Hironobu Sasaki. "ACTIVE SENSING FOR INTELLIGENT ROBOT VISION WITH RANGE IMAGING SENSOR." IEEE, 2010. http://hdl.handle.net/2237/14442.
Amza, Catalin Gheorghe. "Intelligent X-ray imaging inspection system for the food industry." Thesis, De Montfort University, 2002. http://hdl.handle.net/2086/10731.
Dong, Leng. "Intelligent computing applications to assist perceptual training in medical imaging." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/22333.
Sasaki, Hironobu, Toshio Fukuda, Masashi Satomi, and Naoyuki Kubota. "Growing neural gas for intelligent robot vision with range imaging camera." IEEE, 2009. http://hdl.handle.net/2237/13913.
Scott-Jackson, William. "Marker-less respiratory gating for PET imaging with intelligent gate optimisation." Thesis, University of Surrey, 2018. http://epubs.surrey.ac.uk/849418/.
Fukuda, Toshio, Baiqing Sun, Fei Chen, Tomoya Fukukawa, and Hironobu Sasaki. "Active Sensing and Information Structuring for Intelligent Robot Vision with Range Imaging Sensor." IEEE, 2010. http://hdl.handle.net/2237/14441.
Sharif, Mhd Saeed. "An artificial intelligent system for oncological volumetric medical PET classification." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/13095.
關福延 and Folk-year Kwan. "An intelligent approach to automatic medical model reconstruction fromserial planar CT images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243216.
Yang, Kun. "An Intelligent Analysis Framework for Clinical-Translational MRI Research." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1592254585828664.
Esbrand, C. "Feature analysis methods for intelligent breast imaging parameter optimisation using CMOS active pixel sensors." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/19200/.
Bhuiyan, Mofazzal H. "An intelligent system's approach to reservoir characterization in Cotton Valley." Morgantown, W. Va. : [West Virginia University Libraries], 2001. http://etd.wvu.edu/templates/showETD.cfm?recnum=2131.
Title from document title page. Document formatted into pages; contains viii, 92 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 85-88).
Nöjdh, Oscar. "Intelligent boundary extraction for area and volume measurement : Using LiveWire for 2D and 3D contour extraction in medical imaging." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136448.
André, Barbara. "Atlas intelligent pour guider le diagnostic en endomicroscopie : une application clinique de la reconnaissance d'images par le contenu." Phd thesis, École Nationale Supérieure des Mines de Paris, 2011. http://pastel.archives-ouvertes.fr/pastel-00640899.
Curtis, Phillip. "Data Driven Selective Sensing for 3D Image Acquisition." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/30224.
Pincet, Lancelot. "Dynamic excitation systems for quantitative and super-resolved fluorescence microscopy." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP033.
Single Molecule Localization Microscopy (SMLM) is a super-resolution optical technique enabling the observation of biological samples labeled with fluorescent dyes at resolutions well below the diffraction limit. The quality of this imaging heavily relies on the ability to observe molecules individually, requiring precise control of fluorescent dye photophysics for them to emit with a high sparsity in both space and time. Until now, dynamic excitation methods aimed to produce uniform illumination over large fields (200 um x 200 um). However, these types of illumination encounter difficulties in imaging dense biological samples, such as neurons, where the diversity in dye density prevented the generation of a uniform single molecule regime across the entire observed area. To address this issue, I propose a new approach that dynamically adjusts illumination based on sample density. This method combines a novel tri-dynamic optical excitation system with a feedback loop based on density analysis, benefiting from an in-depth study of fluorescent dye photophysics. The intelligent imaging system, where the excitation pattern varies over time, integrates a 2D scanning system, a variable zoom system, and a laser. This allows for the generation of a variety of dynamically changing illumination patterns to adapt to the observed sample and the density of locally detected localizations. This new approach has been validated on various biological samples. Additionally, the dynamic excitation system has also been explored for live samples imaging techniques, such as MSIM or FRAP
Rajagopal, A. "IMAGINE : An Intelligent Electonic Marketplace." Thesis, Indian Institute of Science, 2001. https://etd.iisc.ac.in/handle/2005/254.
Rajagopal, A. "IMAGINE : An Intelligent Electonic Marketplace." Thesis, Indian Institute of Science, 2001. http://hdl.handle.net/2005/254.
Wong, Alison. "Artificial Intelligence for Astronomical Imaging." Thesis, The University of Sydney, 2023. https://hdl.handle.net/2123/30068.
Allen, Axel. "Imagining intelligent artefacts : Myths and a digital sublime regarding artificial intelligence in Swedish newspaper Svenska Dagbladet." Thesis, Stockholms universitet, JMK, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-172782.
Carrass-Milling, Anders, and Camilla Johansson. "Artificiell intelligens inom mammografiscreening : En litteraturstudie." Thesis, Jönköping University, Hälsohögskolan, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-49092.
Recent developments in artificial intelligence (AI) and deep learning (DL) have made diagnostic imaging a prime candidate to adopt the technology. AI in mammography screening aims at promoting health with hopes of higher diagnostic accuracy. The radiographers work is characterized by properly performed imaging and actively updating the profession regarding technical developments and renewed working methods. The aim of this systematic review was to illustrate feasible effects of AI on diagnostic imaging within mammography screening. Through manifest content analysis of results obtained from subject related scientific studies published 2019–2020 in the databases Cinahl and Medline the authors identified and described categories compiled by subcategories with similar contents. Effects within the image interpretation process and diagnostic accuracy describes several perspectives regarding the outputs of AI on diagnostic imaging. AI-systems have proven to be useful in both assisting with image interpretation and reducing the workload for radiologists by disclaiming mammograms with low probability of breast cancer. Most promising effects are seen in the classification of breast tissue and reduction of false positives, but research is challenged by ethical dilemmas and the need for a legal framework, which are areas suggested for future research.
Vilhelmsson, Kajsa, and Tilda Sigurdsson. "Tillämpning av Artificiell Intelligens vid diagnostisering av lungemboli : Litteraturstudie." Thesis, Jönköping University, Hälsohögskolan, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-52968.
COLOMBO, ALESSANDRO. "HIGH PERFORMANCE COMPUTATIONAL INTELLIGENCE FOR COHERENT DIFFRACTION DATA ANALYSIS AND IMAGING." Doctoral thesis, Università degli Studi di Milano, 2018. http://hdl.handle.net/2434/607138.
Rebaud, Louis. "Whole-body / total-body biomarkers in PET imaging." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST047.
This thesis in partnership with Institut Curie and Siemens Healthineers explores the use of Positron Emission Tomography (PET) for cancer prognosis, focusing on non-Hodgkin lymphomas, especially follicular lymphoma (FL) and diffuse large B cell lymphoma (DLBCL). Assuming that current biomarkers computed in PET images overlook significant information, this work focuses on the search for new biomarkers in whole-body PET imaging. An initial manual approach validated a previously identified feature (tumor fragmentation) and explored the prognostic significance of splenic involvement in DLBCL, finding that the volume of splenic involvement does not further stratify patients with such an involvement. To overcome the empirical limitations of the manual search, a semi-automatic feature identification method was developed. It consisted in the automatic extraction of thousands of candidate biomarkers and there subsequent testing by a selection pipeline design to identify features quantifying new prognostic information. The selected biomarkers were then analysed and re-encoded in simpler and more intuitive ways. Using this approach, 22 new image-based biomarkers were identified, reflecting biological information about the tumours, but also the overall health status of the patient. Among them, 10 features were found prognostic of both FL and DLBCL patient outcome. The thesis also addresses the challenge of using these features in clinical practice, proposing the Individual Coefficient Approximation for Risk Estimation (ICARE) model. This machine learning model, designed to reduce overfitting and improve generalizability, demonstrated effectiveness in the HECKTOR 2022 challenge for predicting outcomes from head and neck cancer patients [18F]-PET/CT scans. This model was also found to overfit less than other machine learning methods on an exhaustive comparison using a benchmark of 71 medical datasets. All these developments were implemented in a software extension of a prototype developed by Siemens Healthineers
La, Barbera Giammarco. "Learning anatomical digital twins in pediatric 3D imaging for renal cancer surgery." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT040.
Pediatric renal cancers account for 9% of pediatric cancers with a 9/10 survival rate at the expense of the loss of a kidney. Nephron-sparing surgery (NSS, partial removal of the kidney) is possible if the cancer meets specific criteria (regarding volume, location and extent of the lesion). Indication for NSS is relying on preoperative imaging, in particular X-ray Computerized Tomography (CT). While assessing all criteria in 2D images is not always easy nor even feasible, 3D patient-specific models offer a promising solution. Building 3D models of the renal tumor anatomy based on segmentation is widely developed in adults but not in children. There is a need of dedicated image processing methods for pediatric patients due to the specificities of the images with respect to adults and to heterogeneity in pose and size of the structures (subjects going from few days of age to 16 years). Moreover, in CT images, injection of contrast agent (contrast-enhanced CT, ceCT) is often used to facilitate the identification of the interface between different tissues and structures but this might lead to heterogeneity in contrast and brightness of some anatomical structures, even among patients of the same medical database (i.e., same acquisition procedure). This can complicate the following analyses, such as segmentation. The first objective of this thesis is to perform organ/tumor segmentation from abdominal-visceral ceCT images. An individual 3D patient model is then derived. Transfer learning approaches (from adult data to children images) are proposed to improve state-of-the-art performances. The first question we want to answer is if such methods are feasible, despite the obvious structural difference between the datasets, thanks to geometric domain adaptation. A second question is if the standard techniques of data augmentation can be replaced by data homogenization techniques using Spatial Transformer Networks (STN), improving training time, memory requirement and performances. In order to deal with variability in contrast medium diffusion, a second objective is to perform a cross-domain CT image translation from ceCT to contrast-free CT (CT) and vice-versa, using Cycle Generative Adversarial Network (CycleGAN). In fact, the combined use of ceCT and CT images can improve the segmentation performances on certain anatomical structures in ceCT, but at the cost of a double radiation exposure. To limit the radiation dose, generative models could be used to synthesize one modality, instead of acquiring it. We present an extension of CycleGAN to generate such images, from unpaired databases. Anatomical constraints are introduced by automatically selecting the region of interest and by using the score of a Self-Supervised Body Regressor, improving the selection of anatomically-paired images between the two domains (CT and ceCT) and enforcing anatomical consistency. A third objective of this work is to complete the 3D model of patient affected by renal tumor including also arteries, veins and collecting system (i.e. ureters). An extensive study and benchmarking of the literature on anatomic tubular structure segmentation is presented. Modifications to state-of-the-art methods for our specific application are also proposed. Moreover, we present for the first time the use of the so-called vesselness function as loss function for training a segmentation network. We demonstrate that combining eigenvalue information with structural and voxel-wise information of other loss functions results in an improvement in performance. Eventually, a tool developed for using the proposed methods in a real clinical setting is shown as well as a clinical study to further evaluate the benefits of using 3D models in pre-operative planning. The intent of this research is to demonstrate through a retrospective evaluation of experts how criteria for NSS are more likely to be found in 3D compared to 2D images. This study is still ongoing
COLELLI, GIULIA. "Artificial Intelligence, Mathematical Modeling and Magnetic Resonance Imaging for Precision Medicine in Neurology and Neuroradiology." Doctoral thesis, Università degli studi di Pavia, 2022. https://hdl.handle.net/11571/1468414.
The thesis addresses the possibility of using mathematical methods, simulation techniques, repurposed physical theories and artificial intelligence algorithms to fulfill clinical needs in neuroradiology and neurology. The aim is to describe and to predict disease patterns and its evolution over time as well as to support clinical decision-making processes. The thesis is divided into three parts. Part 1 is related to the development of a Radiomic workflow combined with Machine Learning algorithms in order to predict parameters that quantify muscular anatomical involvement in neuromuscular diseases, with special focus on Facioscapulohumeral dystrophy. The proposed workflow relies on conventional Magnetic Resonance Imaging sequences available in most neuromuscular centers and it can be used as a non-invasive tool to monitor even fine change in neuromuscular disorders and to evaluate longitudinal diseases’ progression over time. Part 2 is about the description of a kinetic model for tumor growth by means of classical tools of statistical mechanics for many-agent systems also taking into account the effects of clinical uncertainties related to patients’ variability in tumor progression. The action of therapeutic protocols is modeled as feedback control at the microscopic level. The controlled scenario allows the dumping of uncertainties associated with the variability in tumors’ dynamics. Suitable numerical methods, based on Stochastic Galerkin formulation of the derived kinetic model, are introduced. Part 3 refers to a still-on going project that attempts to describe a brain portion through a quantum field theory and to simulate its behavior through the implementation of a neural network with an ad-hoc activation function mimicking the biological neuron model response function. Under considered conditions, the brain portion activity can be expressed up to O(6), i.e., up to six fields interaction, as a Gaussian Process. The defined quantum field framework may also be extended to the case of a Non-Gaussian Process behavior, or rather to an interacting quantum field theory in a Wilsonian Effective Field theory approach.
Wallis, David. "A study of machine learning and deep learning methods and their application to medical imaging." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST057.
We first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models. We first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models
Lindström, Sofia, and Maja Becarevic. "Gadoliniumansamling hos patienter med multipel skleros samt implementering av artificiell intelligens vid magnetresonanstomografi." Thesis, Luleå tekniska universitet, Institutionen för hälsa, lärande och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-82796.
Introduction: Approximately 40% of all magnetic resonance imaging (MRI) scans performed in Europe and the United States are performed with gadolinium based contrast agents. Over the past decade, several studies have shown a gadolinium deposition in various structures in the brain. Patients with multiple sclerosis are regularly followed up with MRI with contrast enhancement is the most common method for distinguishing new pathological changes. Developments in technology and methods in artificial intelligence have shown that there is reason to map out whether the radiographers work with examinations and drugs administered to patients can be changed so that the accumulation of gadolinium is prevented. Aim: The purpose of this literature review was to examine the accumulation of gadolinium contrast agents in patients with multiple sclerosis of gadolinium contrast agents in patients with multiple sclerosis and how artificial intelligence can be applied in MRI to reduce the use of gadolinium based contrast agents. Methods: General literature review where scientific articles of a quantitative nature have been searched through the databases CINAHL and PubMed. Results: Both macrocyclic and linear gadolinium based contrast agents are retained in the basal ganglia. With artificial intelligence and CAD, it is possible to obtain data with good quality and at the same time reduce the amount of gadolinium based contrasts to patients. Conclusions: More research on gadolinium accumulation is needed for new routines and methods to be implemented. Accumulation of gadolinium shows that there is reason to continue to develop new methods for monitoring the course of the disease in MS patients. Concerning AI in medical imaging and magnetic resonance imaging, there are many development opportunities that can contribute to the reduction of gadolinium contrast in the future. Continued research in deep learning and CAD can be developed in the future so that the X-ray nurse has a more self-determining function in image production in MRI, but also a more independent work in the management of pharmacies. In addition, this development can contribute to the X - ray nurse's multidisciplinary collaboration with radiologists is strengthened and contributes to a positive development in shorter examination times, better management of patients, optimized examinations, reduction of examination times and shorter care queues.
Jabbar, Shaima Ibraheem. "Automated analysis of ultrasound imaging of muscle and tendon in the upper limb using artificial intelligence methods." Thesis, Keele University, 2018. http://eprints.keele.ac.uk/5433/.
Li, Cui. "Image quality assessment using algorithmic and machine learning techniques." Thesis, Available from the University of Aberdeen Library and Historic Collections Digital Resources. Restricted: no access until June 2, 2014, 2009. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?application=DIGITOOL-3&owner=resourcediscovery&custom_att_2=simple_viewer&pid=26521.
With: An image quality metric based in corner, edge and symmetry maps / Li Cui, Alastair R. Allen. With: An image quality metric based on a colour appearance model / Li Cui and Alastair R. Allen. ACIVS / J. Blanc-Talon et al. eds. 2008 LNCS 5259, 696-707. Includes bibliographical references.
Lee, Jong-Ha. "Tactile sensation imaging system and algorithms for tumor detection." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/151945.
Ph.D.
Diagnosing early formation of tumors or lumps, particularly those caused by cancer, has been a challenging problem. To help physicians detect tumors more efficiently, various imaging techniques with different imaging modalities such as computer tomography, ultrasonic imaging, nuclear magnetic resonance imaging, and mammography, have been developed. However, each of these techniques has limitations, including exposure to radiation, excessive costs, and complexity of machinery. Tissue elasticity is an important indicator of tissue health, with increased stiffness pointing to an increased risk of cancer. In addition to increased tissue elasticity, geometric parameters such as size of a tissue inclusion are also important factors in assessing the tumor. The combined knowledge of tissue elasticity and its geometry would aid in tumor identification. In this research, we present a tactile sensation imaging system (TSIS) and algorithms which can be used for practical medical diagnostic experiments for measuring stiffness and geometry of tissue inclusion. The TSIS incorporates an optical waveguide sensing probe unit, a light source unit, a camera unit, and a computer unit. The optical method of total internal reflection phenomenon in an optical waveguide is adapted for the tactile sensation imaging principle. The light sources are attached along the edges of the waveguide and illuminates at a critical angle to totally reflect the light within the waveguide. Once the waveguide is deformed due to the stiff object, it causes the trapped light to change the critical angle and diffuse outside the waveguide. The scattered light is captured by a camera. To estimate various target parameters, we develop the tactile data processing algorithm for the target elasticity measurement via direct contact. This algorithm is accomplished by adopting a new non-rigid point matching algorithm called "topology preserving relaxation labeling (TPRL)." Using this algorithm, a series of tactile data is registered and strain information is calculated. The stress information is measured through the summation of pixel values of the tactile data. The stress and strain measurements are used to estimate the elasticity of the touched object. This method is validated by commercial soft polymer samples with a known Young's modulus. The experimental results show that using the TSIS and its algorithm, the elasticity of the touched object is estimated within 5.38% relative estimation error. We also develop a tissue inclusion parameter estimation method via indirect contact for the characterization of tissue inclusion. This method includes developing a forward algorithm and an inversion algorithm. The finite element modeling (FEM) based forward algorithm is designed to comprehensively predict the tactile data based on the parameters of an inclusion in the soft tissue. This algorithm is then used to develop an artificial neural network (ANN) based inversion algorithm for extracting various characteristics of tissue inclusions, such as size, depth, and Young's modulus. The estimation method is then validated by using realistic tissue phantoms with stiff inclusions. The experimental results show that the minimum relative estimation errors for the tissue inclusion size, depth, and hardness are 0.75%, 6.25%, and 17.03%, respectively. The work presented in this dissertation is the initial step towards early detection of malignant breast tumors.
Temple University--Theses
Manning, David J. "Applications of signal detection theory to the performance of imaging systems, human observers and artificial intelligence in radiography." Thesis, Lancaster University, 1998. http://eprints.lancs.ac.uk/11591/.
Popli, Labhesh. "AN ATTENTION BASED DEEP NEURAL NETWORK FOR VISUAL QUESTIONANSWERING SYSTEM." Cleveland State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=csu1579015180507068.
Dadi, Kamalaker. "Machine Learning on Population Imaging for Mental Health." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG001.
Mental disorders display a vast heterogeneity across individuals. A fundamental challenge to studying their manifestations or risk factors is that the diagnosis of mental pathological conditions are seldom available in large public health cohorts. Here, we seek to develop brain signatures, biomarkers, of mental disorders. For this, we use ma-chine learning to predict mental-health outcomes through population imaging i. e. with brain imaging (Magnetic Resonance Imaging ( MRI )).Given behavioral or clinical assessments, population imaging can relate unique features of the brain variations to these non-brain self-reported measures based on questionnaires. These non-brain measurements carry a unique description of each individual’s psychological differences which can be linked to psychopathology using statistical methods. This PhD thesis investigates the potential of learning such imaging-based outcomes to analyze mental health. Using machine-learning methods, we conduct an evaluation, both a comprehensive and robust, of population measures to guide high-quality predictions of health outcomes. This thesis is organized into three main parts: first, we present an in-depth study of connectome biomarkers, second, we propose a meaningful data reduction which facilitates large-scale population imaging studies, and finally we introduce proxy measures for mental health. We first set up a thorough benchmark for imaging-connectomes to predict clinical phenotypes. With the rise in the high-quality brain images acquired without tasks, there is an increasing demand in evaluation of existing models for predictions. We performed systematic comparisons relating these images to clinical assessments across many cohorts to evaluate the robustness of population imaging methods for mental health. Our benchmarks emphasize the need for solid foundations in building brain networks across individuals. They outline clear methodological choices. Then, we contribute a new generation of brain functional atlases to facilitate high-quality predictions for mental health. Brain functional atlases are indeed the main bottleneck for prediction. These atlases are built by analyzing large-scale functional brain volumes using scalable statistical algorithm, to have better grounding for outcome prediction. After comparing them with state-of-the-art methods, we show their usefulness to mitigate large-scale data handling problems. The last main contribution is to investigate the potential surrogate measures for health outcomes. We consider large-scale model comparisons using brain measurements with behavioral assessments in an imaging epidemiological cohort, the United Kingdom ( UK ) Biobank. On this complex dataset, the challenge lies in finding the appropriate covariates and relating them to well-chosen outcomes. This is challenging, as there are very few available pathological outcomes. After careful model selection and evaluation, we identify proxy measures that display distinct links to socio-demographics and may correlate with non-pathological conditions like the condition of sleep, alcohol consumption and physical fitness activity. These can be indirectly useful for the epidemiological study of mental health
Sörman, Paulsson Elsa. "Evaluation of In-Silico Labeling for Live Cell Imaging." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-180590.
Bussola, Nicole. "AI for Omics and Imaging Models in Precision Medicine and Toxicology." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/348706.
Wassermann, Demian. "Automated in vivo dissection of white matter structures from diffusion magnetic resonance imaging." Nice, 2010. http://www.theses.fr/2010NICE4066.
The brain is organized in networks that are made up of tracks connecting different regions. These networks are important for the development of brain functions such as language. Lesions and cognitive disorders are sometimes better explained by disconnection mechanisms between cerebral regions than by damage of those regions. Despite several decades of tracing these networks in the brain, our knowledge of cerebral connections has progressed very little since the beginning of the last century. Recently, we have seen a spectacular development of magnetic resonance imaging (MRI) techniques for the study of the living human brain. One technique for exploring white matter (WM) tissue characteristics and pathway in vivo is diffusion MRI (dMRI). Particulary, dMRI tractography facilitates the tracing the WM tracts in vivo. DMRI is a promising technique to explore the anatomical basis of human cognition and its disorders. The motivation of this thesis is the in vivo dissection of the WM. This procedure isolates the WM tracts that play a role in a particular function or disorder of the brain so they can be analysed. Manually performing this task requires a great knowledge of brain anatomy and several hours of work. Hence, the development of a technique to automatically perform the identification of WM structures is of utmost importance. This thesis has several contributions : we develop means for the automatic dissection of WM tracts from dMRI, this is based on a mathematical framework for the WM and its tracts ; using these tools, we develop techniques to analyse the spinal chord and to find group differences in the WM particulary between healthy and schizophrenic subjects
Vétil, Rebeca. "Artificial Intelligence Methods to Assist the Diagnosis of Pancreatic Diseases in Radiology." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT014.
With its increasing incidence and its five- year survival rate (9%), pancreatic cancer could be- come the third leading cause of cancer-related deaths by 2025. These figures are primarily attributed to late diagnoses, which limit therapeutic options. This the- sis aims to assist radiologists in diagnosing pancrea- tic cancer through artificial intelligence (AI) tools that would facilitate early diagnosis. Several methods have been developed. First, a method for the automatic segmentation of the pancreas on portal CT scans was developed. To deal with the specific anatomy of the pancreas, which is characterized by an elonga- ted shape and subtle extremities easily missed, the proposed method relied on local sensitivity adjust- ments using geometrical priors. Then, the thesis tack- led the detection of pancreatic lesions and main pan- creatic duct (MPD) dilatation, both crucial indicators of pancreatic cancer. The proposed method started with the segmentation of the pancreas, the lesion and the MPD. Then, quantitative features were extracted from the segmentations and leveraged to predict the presence of a lesion and the dilatation of the MPD. The method was evaluated on an external test cohort comprising hundreds of patients. Continuing towards early diagnosis, two strategies were explored to de- tect secondary signs of pancreatic cancer. The first approach leveraged large databases of healthy pan- creases to learn a normative model of healthy pan- creatic shapes, facilitating the identification of anoma- lies. To this end, volumetric segmentation masks were embedded into a common probabilistic shape space, enabling zero-shot and few-shot abnormal shape de- tection. The second approach leveraged two types of radiomics: deep learning radiomics (DLR), extracted by deep neural networks, and hand-crafted radiomics (HCR), derived from predefined formulas. The propo- sed method sought to extract non-redundant DLR that would complement the information contained in the HCR. Results showed that this method effectively de- tected four secondary signs of pancreatic cancer: ab- normal shape, atrophy, senility, and fat replacement. To develop these methods, a database of 2800 exa- minations has been created, making it one of the lar- gest for AI research on pancreatic cancer
Preusse, Franziska. "High fluid intelligence and analogical reasoning." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2011. http://dx.doi.org/10.18452/16424.
Bisherige Studien zu zerebralen Korrelaten fluider Intelligenz (fluIQ) haben Aufgaben verwendet, die fluIQ nicht in Reinform erfordern oder haben Probanden mit durchschnittlicher fluIQ (ave-fluIQ) beim Lösen von Intelligenztestaufgaben mit variierenden Schwierigkeitsstufen untersucht und ermöglichen daher keine Aussagen zu interindividuellen Unterschieden in fluIQ. Geometrisches analoges Schließen (GA) beansprucht fluIQ in Reinform und eignet sich daher als differentielles Untersuchungsparadigma. In einer ersten Studie haben wir die zerebralen Korrelate des GA untersucht und nachgewiesen, dass parietale und frontale Hirnregionen involviert sind. Dies steht im Einklang mit der parieto-frontalen Integrationstheorie (P-FIT) der Intelligenz und mit Literaturberichten zu anderen visuell-räumlichen Aufgaben. Aufbauend auf diesen Befunden berichten wir Ergebnisse einer zweiten Studie, in der Schüler mit hoher fluIQ (hi-fluIQ) und ave-fluIQ GA-Aufgaben lösten. In Übereinstimmung mit den Annahmen des P-FIT-Modells konnten wir zeigen, dass GA in beiden Gruppen das parieto-frontale Netzwerk beansprucht. Das Ausmaß der Hirnaktivierung wurde jedoch differentiell durch fluIQ moduliert. Unsere Ergebnisse widersprechen damit teilweise den Postulaten der neuralen Effizienztheorie, die einen negativen Zusammenhang zwischen Hirnaktivierung und Intelligenz annimmt. Wir schlussfolgern, dass dieser Zusammenhang nicht generell einseitig gerichtet ist, sondern die flexible Modulation von Hirnaktivierung charakteristisch für hi-fluIQ ist. Befunde zur Stabilität zerebraler Korrelate von hi-fluIQ in der Jugend waren bisher rar. Um dieses Feld zu beleuchten, haben wir die follow-up-Stabilität zerebraler Korrelate des GA in der hi-fluIQ Gruppe in einer dritten Studie untersucht. Wir konnten zeigen, dass das relevante zerebrale Netzwerk schon mit 17 Jahren etabliert ist und Performanzverbesserungen über die Zeit für eine effizientere Nutzung der verfügbaren zerebralen Ressourcen sprechen.
Janse, van Rensburg Frederick Johannes. "Object recognition and automatic selection in a Robotic Sorting Cell." Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/2609.
This thesis relates to the development of an automated sorting cell as part of a flexible manufacturing line, with the use of object recognition. Algorithms for each of the individual subsections creating the cell, recognition, position calculation and robot integration were developed and tested. The Fourier descriptors object recognition technique is investigated and used. Invariance to scale, rotation or translation of the boundary of an object recognition. Stereoscopy with basic trigonometry is used to calculate the position of recognised objects, after which they are handled by a robot. Integration of the robot into the project environment is done with trigonometry as well as Euler angles. It is shown that a successful, automated sorting cell can be constructed with object recognition. The results show that reliable sorting can be done with available hardware and the algorithms development.
Li, Chao. "Characterising heterogeneity of glioblastoma using multi-parametric magnetic resonance imaging." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287475.
Chambe, Mathieu. "Improving image quality using high dynamic range and aesthetics assessment." Electronic Thesis or Diss., Université de Rennes (2023-....), 2023. http://www.theses.fr/2023URENS015.
To cope with the increasing amount of visual content available, it is important to devise automatic processes that can sort, improve, compress or store images and videos. In this thesis, we propose two different approaches to software-based image improvement. First, we propose a study on existing aesthetics assessment algorithms. These algorithms are based on supervised neural networks. We have collected several datasets of images, and we have tested different models using these images. We report here the performances of such networks, as well as an idea to improve the already trained networks. Our study shows that the features needed to accurately predict the aesthetics of competitive and professional are different but can be learned simultaneously by a single network. In a second time, we propose to work with High Dynamic Range (HDR) images. We present here a new operator to increase the dynamic range of images called HDR-LFNet, that merges the output of existing operators and therefore, consists in far fewer parameters. Besides, we evaluate our method through objective metrics and a user study. We show that our method is on-par with the state-of-the-art according to objective metrics, but is preferred by observers during the user study, while using less resources overall
TRIMBOLI, RUBINA MANUELA. "NEW TRENDS IN BREAST IMAGING FOR BREAST CANCER AND CARDIOVASCULAR RISK." Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/699518.
Penke, Lars. "Neuroscientific approaches to general intelligence and cognitive ageing." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2011. http://dx.doi.org/10.18452/13979.
After an extensive review of what is known about the genetics and neuroscience of general intelligence and a methodological note emphasising the necessity to consider latent variables in cognitive neuroscience studies, exemplified by a re-analysis of published results, the most well-established brain correlate of intelligence, brain size, is revisited from an evolutionary genetic perspective. Estimates of the coefficient of additive genetic variation in brain size suggest that there was no recent directional selection on brain size, questioning its validity as a proxy for intelligence in evolutionary analyses. Instead, correlations of facial fluctuating asymmetry with intelligence and information processing speed in old men suggest that organism-wide developmental stability might be an important cause of individual differences in cognitive ability. The second half of the thesis focuses on cognitive ageing, beginning with a general review. In a sample of over 130 subjects it has then been found that the integrity of different white matter tracts in the brain is highly correlated, allowing for the extraction of a general factor of white matter tract integrity, which is correlated with information processing speed. The only tract not loading highly on this general factor is the splenium of the corpus callosum, which is correlated with changes in intelligence over 6 decades and mediates the effect of the beta2 adrenergic receptor gene (ADRB2) on cognitive ageing, possibly due to its involvement in neuronal compensation processes. Finally, using a novel analytic method for magnetic resonance data, it is shown that more iron depositions in the brain, presumably markers of a history of cerebral microbleeds, are associated with both lifelong-stable intelligence differences and age-related decline in cognitive functioning.
Fraenz, Christoph [Verfasser], Onur [Gutachter] Güntürkün, and Nikolai [Gutachter] Axmacher. "Neural correlates of intelligence and general knowledge obtained by magnetic resonance imaging / Christoph Fraenz ; Gutachter: Onur Güntürkün, Nikolai Axmacher ; Fakultät für Psychologie." Bochum : Ruhr-Universität Bochum, 2019. http://d-nb.info/1201561000/34.
Nasrin, Mst Shamima. "Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676.
Nuzhnaya, Tatyana. "ANALYSIS OF ANATOMICAL BRANCHING STRUCTURES." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/322471.
Ph.D.
Development of state-of-the-art medical imaging modalities such as Magnetic Resonance Imaging, Computed Tomography, Galactography, MR Diffusion Tensor Imaging, and Tomosynthesis plays an important role for visualization and assessment of anatomical structures. Included among these structures are structures of branching topology such as the bronchial tree in chest computed tomography images, the blood vessels in retinal images and the breast ductal network in x-ray galactograms and the tubular bone patterns in dental radiography. Analysis of such images could help reveal abnormalities, assist in estimating a risk of diseases such as breast cancer and COPD, and aid in the development of realistic anatomy phantoms. This thesis aims at the development of a set of automated methods for the analysis of anatomical structures of tree and network topology. More specifically, the two main objectives include (i) the development of analysis framework to explore the association between topology and texture patterns of anatomical branching structures and (ii) the development of the image processing methods for enhanced visualization of regions of interest in anatomical branching structures such as branching nodes.
Temple University--Theses
Sobel, Ryan A. "The Role of Competitive Intelligence in Strategic Decision Making for Commercializing a Novel Endovascular Navigation Technology." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1618854255867602.
Duarte, Everton. "Associação entre volume cerebral e medidas de inteligência em adultos saudáveis: um estudo por ressonância magnética estrutural e volumetria baseada em voxel." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/5/5142/tde-07122011-130244/.
Introduction: The cognitive functions could be influenced by age, both during the development as well as during the decline. Measures of intelligence in general are related to the volume of gray matter (GM) in specific brain areas, and also are under influence of aging process. In children and adolescents the brain areas involved is mainly the prefrontal cortex, while in the elderly other areas, such as the frontal and temporal cortices play an important role. Objective: To identify which brain areas are involved in variations in intelligence measures in a representative sample of large age span, from young adults to healthy elderly. The main hypotheses are: 1) there would be an indirect association between age distribution of GM and surrounding areas of the frontal and temporal cortex, 2) in adults, estimated IQ measures would prove to be stable secondarily to adaptation between fluid intelligence and crystallized functions, 3) in the elderly, measures of IQ estimated would present direct correlation with the distribution of GM involving the temporal cortex and limbic, 4) in the elderly, measures of fluid intelligence would show a direct correlation with the distribution of GM and pre frontal cortices, 5) in the elderly, measures of crystallized intelligence would not present significant correlations with the distribution of GM. Methods: Brain images scans were obtained from a representative sample with large age span. We investigated a sample of 258 subjects between 18 and 75 who met the criteria for inclusion / exclusion, and for which it is possible to estimate the IQ subtests by the WASI. Correlation statistical analyses were performed using voxel-based methodology with Statistical Parametric Map Software, and reported only if survived multiple comparison correction. All results were corrected for confounding variables (protocol, MRI scan and total brain volume). Results: We identified correlations between IQ and estimated fluid intelligence in medial temporal and limbic regions bilateral in the elderly. In the adult population we also identified correlations between crystallized intelligence involving the frontal and prefrontal right. We also observed that there was loss of GM in adults involving the left prefrontal regions and frontal. Conclusion: Measures of fluid and crystallized intelligence showed direct correlation with the total brain volume, and specifically in the frontal cortex, bilateral prefrontal, and temporal and limbic regions
Oukhatar, Fatima. "Design, synthesis and characterization of neurotransmitter responsive probes for magnetic resonance and optical imaging." Thesis, Orléans, 2012. http://www.theses.fr/2012ORLE2076/document.
In spite of the key role of neurotransmitters (NTs) in signal transduction, their non-invasive in vivo monitoring remains an important challenge. Magnetic resonance imaging (MRI) has recently been demonstrated as a promising technique to non-invasively visualize physiological events with excellent temporal and spatial resolution. In particular, smart MRI contrast agents that are able to report on the physico-chemical status of the tissues, start to have a strong impact in neuroscience. The objective of this work was the design, synthesis and in vitro characterization of a series of lanthanide-based probes responsive to NTs with the aim to track in vivo concentration changes of NTs using MR or optical imaging. The design of our imaging probes relies on a dual binding approach of zwitterionic NTs to the Ln3+ complexes, involving interactions (i) between a positively charged Ln3+ chelate and the carboxylate function of the NTs and (ii) between an azacrown ether appended on the chelate and the amine group of the neurotransmitters. Some of the novel contrast agents were found to exhibit high relaxivities and a remarkable relaxivity response towards NTs, though little selectivity against bicarbonate. In order to apply a bimodal MRI/optical imaging approach, we have also incorporated a benzophenone moiety into the chelate to sensitize the near-infrared emitting Ln3+ ions. The Yb3+ analogue proved to be highly sensitive to NTs