2,751 research outputs found
Predictive models based on Support Vector Machines: whole-brain versus regional analysis of structural MRI in the Alzheimer’s disease
Decision-making systems trained on structural magnetic resonance imaging data of subjects affected by the Alzheimer's disease (AD) and healthy controls (CTRL) are becoming widespread prognostic tools for subjects with mild cognitive impairment (MCI). This study compares the performances of three classification methods based on support vector machines (SVMs), using as initial sets of brain voxels (ie, features): (1) the segmented grey matter (GM); (2) regions of interest (ROIs) by voxel-wise t-test filtering; (3) parceled ROIs, according to prior knowledge. The recursive feature elimination (RFE) is applied in all cases to investigate whether feature reduction improves the classification accuracy. We analyzed more than 600 AD Neuroimaging Initiative (ADNI) subjects, training the SVMs on the AD/CTRL dataset, and evaluating them on a trial MCI dataset. The classification performance, evaluated as the area under the receiver operating characteristic curve (AUC), reaches AUC = (88.9 ± .5)% in 20-fold cross-validation on the AD/CTRL dataset, when the GM is classified as a whole. The highest discrimination accuracy between MCI converters and nonconverters is achieved when the SVM-RFE is applied to the whole GM: with AUC reaching (70.7 ± .9)%, it outperforms both ROI-based approaches in predicting the AD conversion
Regions of interest computed by SVM wrapped method for Alzheimer’s disease examination from segmented MRI
Accurate identification of the most relevant brain regions linked to Alzheimer’s disease (AD) is crucial in order to improve diagnosis techniques and to better understand this neurodegenerative process. For this purpose, statistical classification is suitable. In this work, a novel method based on support vector machine recursive feature elimination (SVM-RFE) is proposed to be applied on segmented brain MRI for detecting the most discriminant AD regions of interest (ROIs). The analyses are performed both on gray and white matter tissues, achieving up to 100% accuracy after classification and outperforming the results obtained by the standard t-test feature selection. The present method, applied on different subject sets, permits automatically determining high-resolution areas surrounding the hippocampal area without needing to divide the brain images according to any common template.This work was partly supported by the MICINN under the TEC2012-34306 project and the ConsejerĂa de InnovaciĂłn, Ciencia y Empresa (Junta de AndalucĂa, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904).This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence
A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence
IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION
Techniques for processing and analysing images and medical data have become
the main’s translational applications and researches in clinical and pre-clinical
environments. The advantages of these techniques are the improvement of diagnosis
accuracy and the assessment of treatment response by means of quantitative biomarkers
in an efficient way. In the era of the personalized medicine, an early and
efficacy prediction of therapy response in patients is still a critical issue.
In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high
quality detailed images and excellent soft-tissue contrast, while Computerized
Tomography (CT) images provides attenuation maps and very good hard-tissue
contrast. In this context, Positron Emission Tomography (PET) is a non-invasive
imaging technique which has the advantage, over morphological imaging techniques,
of providing functional information about the patient’s disease.
In the last few years, several criteria to assess therapy response in oncological
patients have been proposed, ranging from anatomical to functional assessments.
Changes in tumour size are not necessarily correlated with changes in tumour
viability and outcome. In addition, morphological changes resulting from therapy
occur slower than functional changes. Inclusion of PET images in radiotherapy
protocols is desirable because it is predictive of treatment response and provides
crucial information to accurately target the oncological lesion and to escalate the
radiation dose without increasing normal tissue injury. For this reason, PET may be
used for improving the Planning Treatment Volume (PTV). Nevertheless, due to the
nature of PET images (low spatial resolution, high noise and weak boundary),
metabolic image processing is a critical task.
The aim of this Ph.D thesis is to develope smart methodologies applied to the
medical imaging field to analyse different kind of problematic related to medical
images and data analysis, working closely to radiologist physicians.
Various issues in clinical environment have been addressed and a certain amount
of improvements has been produced in various fields, such as organs and tissues
segmentation and classification to delineate tumors volume using meshing learning
techniques to support medical decision.
In particular, the following topics have been object of this study:
• Technique for Crohn’s Disease Classification using Kernel Support Vector
Machine Based;
• Automatic Multi-Seed Detection For MR Breast Image Segmentation;
• Tissue Classification in PET Oncological Studies;
• KSVM-Based System for the Definition, Validation and Identification of the
Incisinal Hernia Reccurence Risk Factors;
• A smart and operator independent system to delineate tumours in Positron
Emission Tomography scans;
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• Active Contour Algorithm with Discriminant Analysis for Delineating
Tumors in Positron Emission Tomography;
• K-Nearest Neighbor driving Active Contours to Delineate Biological Tumor
Volumes;
• Tissue Classification to Support Local Active Delineation of Brain Tumors;
• A fully automatic system of Positron Emission Tomography Study
segmentation.
This work has been developed in collaboration with the medical staff and
colleagues at the:
• Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi
(DIBIMED), University of Palermo
• Cannizzaro Hospital of Catania
• Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) Centro Nazionale
delle Ricerche (CNR) of CefalĂą
• School of Electrical and Computer Engineering at Georgia Institute of
Technology
The proposed contributions have produced scientific publications in indexed
computer science and medical journals and conferences. They are very useful in
terms of PET and MRI image segmentation and may be used daily as a Medical
Decision Support Systems to enhance the current methodology performed by
healthcare operators in radiotherapy treatments.
The future developments of this research concern the integration of data acquired
by image analysis with the managing and processing of big data coming from a wide
kind of heterogeneous sources
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