3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

Inter-individual cognitive variability in children with Asperger's syndrome

Multiple studies have tried to establish the distinctive profile of individuals with Asperger's syndrome (AS). However, recent reports suggest that adults with AS feature heterogeneous cognitive profiles. The present study explores inter-individual variability in children with AS through group comparison and multiple case series analysis. All participants completed an extended battery including measures of fluid and crystallized intelligence, executive functions, theory of mind, and classical neuropsychological tests. Significant group differences were found in theory of mind and other domains related to global information processing. However, the AS group showed high inter-individual variability (both sub- and supra-normal performance) on most cognitive tasks. Furthermore, high fluid intelligence correlated with less general cognitive impairment, high cognitive flexibility, and speed of motor processing. In light of these findings, we propose that children with AS are characterized by a distinct, uneven pattern of cognitive strengths and weaknesses.Fil: González Gadea, María Luz. Universidad Diego Portales; Chile. Universidad Favaloro; Argentina. Instituto de Neurología Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tripicchio, Paula. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; ArgentinaFil: Rattazzi del Carril, Alexia. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; ArgentinaFil: Báez Buitrago, Sandra Jimena. Universidad Favaloro; Argentina. Universidad Diego Portales; Chile. Universidad Catolica Argentina; Argentina. Instituto de Neurología Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Marino, Julián Carlos. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Roca, María. Universidad Favaloro; Argentina. Instituto de Neurología Cognitiva; Argentina. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Manes, Facundo Francisco. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; Argentina. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centre of Excellence in Cognition and its Disorders; AustriaFil: Ibanez Barassi, Agustin Mariano. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; Argentina. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centre of Excellence in Cognition and its Disorders; Austria. Universidad Autonoma del Caribe; Colombi

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl

Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user pro le by following the principles of the I-Change model and maintaining the bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.European Union's Horizon 2020 Research and Innovation Programme under Grant 68112

The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence

Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. Current state-of-the-art research, however, fails by far to achieve this ultimate goal. As one of the main obstacles to be overcome we perceive the question how symbolic knowledge can be encoded by means of connectionist systems: Satisfactory answers to this will naturally lead the way to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page