Artificial neural network-statistical approach for PET volume analysis and classification

Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

Intelligent classification of ammonia concentration based on odor profile

This thesis presents the intelligent classification of ammonia concentration based on the standard of oil and gas industries wastewater discharge. The intelligent classification using signal processing is a well-known technique in many applications and as well in the oil and gas industry. The intelligent classification technique for ammonia concentration classification is a demanding technique especially in the environmental sector. Ammonia solution properties and ammonia solution preparations were studied in this thesis which commonly used in industry. The objectives of this thesis are to develop an intelligence classification of ammonia concentration based on the oil and gas industry wastewater discharge schedule and to analyze performance of the intelligent classification of ammonia concentration based on the oil and gas industry wastewater discharge schedule. In this thesis the ammonia odor profile has been pre-identified by chemist using four sensor array. The ammonia concentration was validated using a commercialized gas sensor and spectrophotometer to cross-validated e-nose instrument. The odor profile from two different samples; high (20 ppm and 25 ppm) and low (5 ppm, 10 ppm and 1 5ppm) concentration that have been normalized and visualized in a 2D plot to extract the unique patterns. The variance of the low and high concentration of ammonia odor profile has been identified as different group samples. This group samples have been analyzed statistically using Boxplot, calibration curve and proximity matrix, The thesis describes the statistical techniques to visualize the pattern and using mean features to classify between the low and high concentration. Two intelligent classification techniques have been used which are Artificial Neural Network (ANN) using the back-propagation approaches and then, the result of ANN model was cross-validated.using CBR. Both ANN model and CBR classifier have been measured using several performance measures. From the results, it is observed that ANN model and CBR classifier are capable of classifying 100% of ammonia concentration odor profile from the water. The results can also significantly reduce the cost and time, and improve product reliability and customer confidence

Metabolically active volumes automatic delineation methodologies in PET imaging: review and perspectives

International audiencePET imaging is now considered a gold standard tool in clinical oncology, especially for diagnosis purposes. More recent applications such as therapy follow up or tumor targeting in radiotherapy require a fast, accurate and robust metabolically active tumor volumes on emission images, which cannot be obtained through manual contouring. This clinical need has sprung a large number of methodological developments regarding automatic methods to defined tumor volumes on PET images. This paper reviews most of the methodologies that have been recently proposed and discusses their framework and methodological and/or clinical validation. Perspectives regarding the future work to be done are also suggested

Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images

Positron emission tomography (PET)-Computed tomography (CT) plays an important role in cancer management. As a multi-modal imaging technique it provides both functional and anatomical information of tumor spread. Such information improves cancer treatment in many ways. One important usage of PET-CT in cancer treatment is to facilitate radiotherapy planning, for the information it provides helps radiation oncologists to better target the tumor region. However, currently most tumor delineations in radiotherapy planning are performed by manual segmentation, which consumes a lot of time and work. Most computer-aided algorithms need a knowledgeable user to locate roughly the tumor area as a starting point. This is because, in PET-CT imaging, some tissues like heart and kidney may also exhibit a high level of activity similar to that of a tumor region. In order to address this issue, a novel co-segmentation method is proposed in this work to enhance the accuracy of tumor segmentation using PET-CT, and a localization algorithm is developed to differentiate and segment tumor regions from normal regions. On a combined dataset containing 29 patients with lung tumor, the combined method shows good segmentation results as well as good tumor recognition rate

Differentiation of Alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach

Nous avons utilisé l'imagerie TEP avec les traceurs F18-FDG et AV45 en conjonction avec les méthodes de classification du domaine du "Machine Learning". Les images ont été acquises en mode dynamique, une image toutes les 5 minutes. Les données ont été transformées par Analyse en Composantes Principales et Analyse en Composantes Indépendantes. Les images proviennent de trois sources différentes: la base de données ADNI (Alzheimer's Disease Neuroimaging Initiative) et deux protocoles réalisés au sein du centre TEP de l'hôpital Purpan. Pour évaluer la performance de la classification nous avons eu recours à la méthode de validation croisée LOOCV (Leave One Out Cross Validation). Nous donnons une comparaison entre les deux méthodes de classification les plus utilisées, SVM (Support Vector Machine) et les réseaux de neurones artificiels (ANN). La combinaison donnant le meilleur taux de classification semble être SVM et le traceur AV45. Cependant les confusions les plus importantes sont entre les patients MCI et les sujets normaux. Les patients Alzheimer se distinguent relativement mieux puisqu'ils sont retrouvés souvent à plus de 90%. Nous avons évalué la généralisation de telles méthodes de classification en réalisant l'apprentissage sur un ensemble de données et la classification sur un autre ensemble. Nous avons pu atteindre une spécificité de 100% et une sensibilité supérieure à 81%. La méthode SVM semble avoir une meilleure sensibilité que les réseaux de neurones. L'intérêt d'un tel travail est de pouvoir aider à terme au diagnostic de la maladie d'Alzheimer.We used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes.The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set . We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease

Évaluation de la correction du mouvement respiratoire sur la détection des lésions en oncologie TEP

La tomographie par émission de positons (TEP) est une méthode d imagerie clinique en forte expansion dans le domaine de l oncologie. De nombreuses études cliniques montrent que la TEP permet, d une part de diagnostiquer et caractériser les lésions cancéreuses à des stades plus précoces que l imagerie anatomique conventionnelle, et d autre part d évaluer plus rapidement la réponse au traitement. Le raccourcissement du cycle comprenant le diagnostic, la thérapie, le suivi et la réorientation thérapeutiques contribue à augmenter le pronostic vital du patient et maîtriser les coûts de santé. La durée d un examen TEP ne permet pas de réaliser une acquisition sous apnée. La qualité des images TEP est par conséquent affectée par les mouvements respiratoires du patient qui induisent un flou dans les images. Les effets du mouvement respiratoire sont particulièrement marqués au niveau du thorax et de l abdomen. Plusieurs types de méthode ont été proposés pour corriger les données de ce phénomène, mais elles demeurent lourdes à mettre en place en routine clinique. Des travaux récemment publiés proposent une évaluation de ces méthodes basée sur des critères de qualité tels que le rapport signal sur bruit ou le biais. Aucune étude à ce jour n a évalué l impact de ces corrections sur la qualité du diagnostic clinique. Nous nous sommes focalisés sur la problématique de la détection des lésions du thorax et de l'abdomen de petit diamètre et faible contraste, qui sont les plus susceptibles de bénéficier de la correction du mouvement respiratoire en routine clinique. Nos travaux ont consisté dans un premier temps à construire une base d images TEP qui modélisent un mouvement respiratoire non-uniforme, une variabilité inter-individuelle et contiennent un échantillonnage de lésions de taille et de contraste variable. Ce cahier des charges nous a orientés vers les méthodes de simulation Monte Carlo qui permettent de contrôler l ensemble des paramètres influençant la formation et la qualité de l image. Une base de 15 modèles de patient a été créée en adaptant le modèle anthropomorphique XCAT sur des images tomodensitométriques (TDM) de patients. Nous avons en parallèle développé une stratégie originale d évaluation des performances de détection. Cette méthode comprend un système de détection des lésions automatisé basé sur l'utilisation de machines à vecteurs de support. Les performances sont mesurées par l analyse des courbes free-receiver operating characteristics (FROC) que nous avons adaptée aux spécificités de l imagerie TEP. L évaluation des performances est réalisée sur deux techniques de correction du mouvement respiratoire, en les comparant avec les performances obtenues sur des images non corrigées ainsi que sur des images sans mouvement respiratoire. Les résultats obtenus sont prometteurs et montrent une réelle amélioration de la détection des lésions après correction, qui approche les performances obtenues sur les images statiques.Positron emission tomography (PET) is nuclear medicine imaging technique that produces a three-dimensional image of functional processes in the body. The system detects pairs of gamma rays emitted by a tracer, which is introduced into the body. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. Respiratory motion in emission tomography leads to image blurring especially in the lower thorax and the upper abdomen, influencing this way the quantitative accuracy of PET measurements as well a leading to a loss of sensitivity in lesion detection. Although PET exams are getting shorter thanks to the improvement of scanner sensitivity, the current 2-3 minutes acquisitions per bed position are not yet compatible with patient breath-holding. Performing accurate respiratory motion correction without impairing the standard clinical protocol, ie without increasing the acquisition time, thus remains challenging. Different types of respiratory motion correction approaches have been proposed, mostly based on the use of non-rigid deformation fields either applied to the gated PET images or integrated during an iterative reconstruction algorithm. Evaluation of theses methods has been mainly focusing on the quantification and localization accuracy of small lesions, but their impact on the clinician detection performance during the diagnostic task has not been fully investigated yet. The purpose of this study is to address this question based on a computer assisted detection study. We evaluate the influence of two motion correction methods on the detection of small lesions in human oncology FDG PET images. This study is based on a series of realistic simulated whole-body FDG images based on the XCAT model. Detection performance is evaluated with a computer-aided detection system that we are developing for whole-body PET/CT images. Detection performances achieved with these two correction methods are compared with those achieved without correction, ie. with respiration average PET images as well as with reference images that do not model respiration effects. The use of simulated data makes possible the creation of theses perfectly corrected images and the definition of known lesions locations that serve as a reference.VILLEURBANNE-DOC'INSA-Bib. elec. (692669901) / SudocSudocFranceF

Modeling the risks of age-related eye diseases in a population in South India

The objective of this research was to determine whether an artificial intelligence methodology such as artificial neural network (ANN), a new type of predictive model offers an increased performance over a conventional logistic regression model (LR) in predicting the ranking of risk factors for irreversible age-related chronic eye diseases age-related macular degeneration (AMD), diabetic retinopathy (DR), primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) in a South Indian population. The LR and ANN models were derived and validated for their respective models predictive accuracy based on a sample (n=3,723) aged >=40 years old by using a large scale population-based epidemiologic study. Sub-population data were drawn from this sample by appropriate standard techniques that used for modeling. The LR based risk score models (RS) were derived and the model fit was assessed in a standard manner including the bootstrap method for internal validity. The ANN model was built by using the multi-layer feed-forward back propagation network. The ANN models predictive ability was compared with that of traditional model with respect to the Area under the Receiver Operating Characteristic Curve (AUROC). The sensitivity and specificity of the fitted models with a threshold criterion ranged from 70% to nearly 99% overall for all models. The ANN model outperformed the traditional LR model in a sub-population analysis in predicting AMD and DR. The predictive accuracy of ANN and LR model in predicting AMD was statistically significant (AUROC=89% vs 79%; p=10 year (RS ranged from 29 to 42) was a highest priority predictor for DR. The modifiable risk factor intraocular pressure was in order of highest priority predictor for POAG and PACG. Population attributable risk percentage and population attributable fractions revealed that there is an urgent need of prioritizing modifying the modifiable factors as a public health approach. This was supported by a sensitivity analysis of the ANN model which indicated the relative importance of prioritizing modifiable risk factors on which to base preventive interventions to reduce the impact of onset or progression of these diseases

Artificial Neural Network-Based System for PET Volume Segmentation

Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results