Fetal-BET: Brain Extraction Tool for Fetal MRI

Fetal brain extraction is a necessary first step in most computational fetal brain MRI pipelines. However, it has been a very challenging task due to non-standard fetal head pose, fetal movements during examination, and vastly heterogeneous appearance of the developing fetal brain and the neighboring fetal and maternal anatomy across various sequences and scanning conditions. Development of a machine learning method to effectively address this task requires a large and rich labeled dataset that has not been previously available. As a result, there is currently no method for accurate fetal brain extraction on various fetal MRI sequences. In this work, we first built a large annotated dataset of approximately 72,000 2D fetal brain MRI images. Our dataset covers the three common MRI sequences including T2-weighted, diffusion-weighted, and functional MRI acquired with different scanners. Moreover, it includes normal and pathological brains. Using this dataset, we developed and validated deep learning methods, by exploiting the power of the U-Net style architectures, the attention mechanism, multi-contrast feature learning, and data augmentation for fast, accurate, and generalizable automatic fetal brain extraction. Our approach leverages the rich information from multi-contrast (multi-sequence) fetal MRI data, enabling precise delineation of the fetal brain structures. Evaluations on independent test data show that our method achieves accurate brain extraction on heterogeneous test data acquired with different scanners, on pathological brains, and at various gestational stages. This robustness underscores the potential utility of our deep learning model for fetal brain imaging and image analysis.Comment: 10 pages, 6 figures, 2 TABLES, This work has been submitted to the IEEE Transactions on Medical Imaging for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

The reality of myoelectric prostheses : understanding what makes these devices difficult for some users to control

Users of myoelectric prostheses can often find them difficult to control. This can lead to passive-use of the device or total rejection, which can have detrimental effects on the contralateral limb due to overuse. Current clinically available prostheses are ‘open loop’ systems, and although considerable effort has been focused on developing biofeedback to “close the loop”, there is evidence from laboratory-based studies that other factors, notably improving predictability of response, may be as, if not more, important. Interestingly, despite a large volume of research aimed at improving myoelectric prostheses, it is not currently known which aspect of clinically available systems has the greatest impact on overall functionality and everyday usage. A protocol has therefore been designed to assess EMG skill of the user and predictability of the prosthesis response as significant parts of the control chain, and to relate these to functionality and everyday usage. Here we present the protocol and results from early pilot work. A set of experiments has been developed. Firstly to characterize user skill in generating the required level of EMG signal, as well as the speed with which users are able to make the decision to activate the appropriate muscles. Secondly, to measure unpredictability introduced at the skin-electrode interface, in order to understand the effects of the socket mounted electrode fit under different loads on the variability of time taken for the prosthetic hand to respond. To evaluate prosthesis user functionality, four different outcome measures are assessed. Using a simple upper limb functional task prosthesis users are assessed for (1) success of task completion, (2)task duration, (3) quality of movement, and (4) gaze behavior. To evaluate everyday usage away from the clinic, the symmetricity of their real-world arm use is assessed using activity monitoring. These methods will later be used to assess a prosthesis user cohort, to establish the relative contribution of each control factor to the individual measures of functionality and everyday usage (using multiple regression models). The results will support future researchers, designers and clinicians in concentrating their efforts on the area which will have the greatest impact on improving prosthesis use

Intending to be aggressive : applying the theory of planned behaviour to reactive and instrumental adolescent aggression

Adolescents’ intentions to behave in both reactive and instrumental aggression were assessed using the Theory of Planned Behaviour (TPB; I. Ajzen, 1985). Along with examining the TPB, perceptions of self-efficacy (A. Bandura, 1982) towards both types of aggression were also assessed. Self-report questionnaires were administered to 162 grade 10 to 12 students in two independent school districts. Using Path Analysis, the TPB was shown to significantly explain both instrumental and reactive aggression. In the context of reactive aggression, attitudes were found to have the greatest influence on intentions to behave aggressively. As for instrumental aggression, self-efficacy was found to have the greatest influence on intentions. Overall, the results of this study provide support for using the TPB to explain adolescent aggression. In addition, this study further demonstrates the value of distinguishing between reactive and instrumental functions of aggression