A general variance predictor for Cavalieri slices

A general variance predictor is presented for a Cavalieri design with slices of an arbitrary thickness t ≥ 0. So far, prediction formulae have been available either for measurement functions with smoothness constant q = 0, 1, … , and t ≥ 0, or for fractional q ∈ [0, 1] with t = 0. Because the possibility of using a fractional q adds flexibility to the variance prediction, we have extended the latter for any q ∈ [0, 1] and t ≥ 0. Empirical checks with previously published human brain data suggest an improved performance of the new prediction formula with respect to the hitherto available ones.     

Résumés du congrès annuel 2007 de la SCCCSC.

Presents a collection of abstracts from the 2007 Canadian Society for Brain, Behaviour and Cognitive Science (CSBBCS) Annual Meeting. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Brain mass estimation by head circumference and body mass methods in neonatal glycaemic modelling and control

Introduction

Hyperglycaemia is a common complication of stress and prematurity in extremely low-birth-weight infants. Model-based insulin therapy protocols have the ability to safely improve glycaemic control for this group. Estimating non-insulin-mediated brain glucose uptake by the central nervous system in these models is typically done using population-based body weight models, which may not be ideal.

Method

A head circumference-based model that separately treats small-for-gestational-age (SGA) and appropriate-for-gestational-age (AGA) infants is compared to a body weight model in a retrospective analysis of 48 patients with a median birth weight of 750 g and median gestational age of 25 weeks. Estimated brain mass, model-based insulin sensitivity (S_I) profiles, and projected glycaemic control outcomes are investigated. SGA infants (5) are also analyzed as a separate cohort.

Results

Across the entire cohort, estimated brain mass deviated by a median 10% between models, with a per-patient median difference in S_I of 3.5%. For the SGA group, brain mass deviation was 42%, and per-patient S_I deviation 13.7%. In virtual trials, 87–93% of recommended insulin rates were equal or slightly reduced (Δ < 0.16 mU/h) under the head circumference method, while glycaemic control outcomes showed little change.

Conclusion

The results suggest that body weight methods are not as accurate as head circumference methods. Head circumference-based estimates may offer improved modelling accuracy and a small reduction in insulin administration, particularly for SGA infants.     

基于ERP信号的目标检测研究综述

目标检测技术应用广泛,现有的基于计算机视觉的目标检测方法由于目标遮挡、光照强弱等因素难以适应复杂场景的需求。而人脑的高级认知能力和快速感知能力在处理复杂情况时具有一定的优势。基于事件相关电位（Event-Related Potentials,ERP）的脑机接口（Brain Computer Interface,BCI）系统与特定事件相关,可检测独立于自发脑电的高级认知活动,是当前人工智能领域的研究热点之一。针对基于ERP信号的目标检测各个环节进行了研究现状的全面归纳,梳理了以快速串行视觉呈现（Rapid Serial Visual Presentation task,RSVP）为主的实验范式,包括呈现模式、目标视场角、目标复杂度等设计因素。总结了脑电信号分析中的预处理方法、特征提取和特征分类算法,介绍了其在人脸识别、军事作战、医学分析等领域中的初步应用。探讨了目前研究中存在的问题和挑战并展望未来的研究方向与应用前景。     

Empathy and Modern Technology: A Neuroergonomics Perspective

The primary purpose of this paper is to discuss the role of empathy in the design of advanced systems in manufacturing and service industries in order to ensure suitable working conditions for employees from the social and technological point of view. The origins and components of empathy are briefly reviewed. The neural underpinnings of three components of empathy, including cognitive, emotional, and behavioral aspects, are considered in the context of human–human and human–machine interactions, as well as design of working environments. Finally, the potential advantages of applying empathy‐related knowledge to the design and development of human‐centered technology are discussed.     

An efficient hybrid multicast transport protocol for collaborative virtual environment with networked haptic

In recent years, we have witnessed a growing interest in the synchronous collaboration based class of applications. Several techniques for collaborative virtual environments (CVE), haptic, audio and visual environments (C-HAVE) have been designed. However, several challenging issues remain to be resolved before CVE and C-HAVE become a common place. In this paper, we focus on applications that are based on closely coupled and highly synchronized haptic tasks that require a high-level of coordination among the participants. Four main protocols have been designed to resolve the synchronization issues in such environments: the synchronous collaboration transport protocol, the selective reliable transmission protocol, the reliable multicast transport protocol and the scalable reliable multicast. While these four protocols have shown good performance for CVE and C-HAVE class of applications, none of these protocols has been able to meet all of the basic CVE requirements, i.e., scalability, reliability, synchronization, and minimum delay. In this paper, we present a hybrid protocol that is able to satisfy all of the CVE and C-HAVE requirements and discuss its implementation and results in two tele-surgery applications. This work is partially supported by Grants from Canada Research Chair Program, NSERC, OIT/Ontario Distinguished Researcher Award, Early Research Award and ORNEC Research Grant.     

Intensity standardization simplifies brain MR image segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model-based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.     

Membrane computing and brain modelling

We briefly discuss variants of (extended) spiking neural P systems that combine features from the areas of membrane computing and spiking neurons. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

An Automated Brain Image Analysis System for Brain Cancer using Shearlets

In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of features that includes Gray Level Co-occurrence Matrix (GLCM) based features, Histograms of Positive Shearlet Coefficients (HPSC), and Histograms of Negative Shearlet Coefficients (HNSC) are estimated. The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers; k-Nearest Neighbor (kNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. The output of individual trained classifiers for a testing input is hybridized to take a final decision. The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data (REMBRANDT) database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.