THE LEFT HEMISPHERE’S STRUCTURAL CONNECTIVITY FOR THE INFERIOR FRONTAL GYRUS, STRIATUM, AND THALAMUS, AND INTRA-THALAMIC TOPOGRAPHY

The neuroanatomy of language cognition has an extensive history of scientific interest and inquiry. Over a century of behavioral lesion studies and decades of functional neuroimaging research have established the left hemisphere’s inferior frontal gyrus (IFG) as a critical region for speech and language processing. This region’s subcortical projections are thought to be instrumental for supporting and integrating the cognitive functions of the language network. However, only a subset of these projections have been shown to exist in humans, and structural evidence of pars orbitalis’ subcortical circuitry has been limited to non-human primates. This thesis demonstrates direct, intra-structural connectivity of each of the left IFG’s gyral regions with the thalamus and the putamen in humans, using high-angular, deterministic tractography. Novel processing and analysis methods elucidated evidence of predominantly segregated cortical circuits within the thalamus, and suggested the presence of parallel circuits for motor/language integration along the length of the putamen

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

6-DoF Grasp Learning in Partially Observable Cluttered Scenes

The key element of the efficient interaction of an intelligent robot with its im- mediate environment is object manipulation - a task that current data-driven methods reshape into various methods aimed at object localization, classification, segmentation, and grasp pose estimation. This work is concerned with the grasp pose estimation, namely with the implications of 6-DoF grasp pose estimation for partially visible cluttered scenes. In this thesis, two methods are proposed to address the problem of collision management of the grasp proposals and the full target scene due to the partial visibility and cluttered nature of a scene. The first explores the possibility of embedding input data with differential geometrical shape information, namely the modified mean curvature measure, to improve the qualitative results of grasp estimation. The second method proposes a supervisor network architecture termed Collision-GraspNet that classifies grasp proposals with respect to collision with the scene, including its occluded parts, and improves the invalid proposals via iterative pose sampling. The first proposed approach is tested on the Contact-GraspNet model and compared with GraspNet architecture baseline performance. In its turn, Collision-GraspNet is compared with an analytical proposal filtering approach employed by GraspNet, and evaluated in three stages using various datasets. Grasp supervisor architecture Collision-GraspNet outperformed the respective analytical approach and showed high confidence threshold flexibility. However, curvature-embedded data failed to improve upon the baseline model performance

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

Using Statistics, Computational Modelling and Artificial Intelligence Methods to Study and Strengthen the Link between Kinematic Impacts and mTBIs

Mild traumatic brain injuries (mTBIs) are frequently occurring, yet poorly understood, injuries in sports (e.g., ice hockey) and other physical recreation activities where head impacts occur. Helmets are essential pieces of equipment used to protect participants’ heads from mTBIs. Evaluating the performance of helmets to prevent mTBIs using simulations on anatomically accurate computational head finite element models is critically important for advancing the development of safer helmets. Advancing the level of detail in, and access to, such models, and their continued validation through state-of-the-art brain imaging methods and traditional head injury assessment procedures, is also essential to improve safety. The significant research contributions in this thesis involve evaluating the decrease in blunt impact-induced brain axon fiber tract strains that various helmets provide by studying outputs of existing finite element brain models and implementing open-source artificial intelligence technology to create a novel pipeline for predicting such strains