Deep Neural Network with l2-norm Unit for Brain Lesions Detection

Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations. Deep Learning recently has shown promising progress in many application fields, which motivates us to apply this technology for such important problem. In this paper, we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN). In order to investigate the applicability, we applied our approach on several brain diseases including high and low-grade glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic Resonance Images (MRI) have been applied as an input for the analysis. We proposed a new operating unit which receives features from several projections of a subset units of the bottom layer and computes a normalized l2-norm for next layer. We evaluated the proposed approach on two different CNN architectures and number of popular benchmark datasets. The experimental results demonstrate the superior ability of the proposed approach.Comment: Accepted for presentation in ICONIP-201

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images

Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure.Comment: 15 pages, 12 figure

Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications

In the human brain, essential iron molecules for proper neurological functioning exist in transferrin (tf) and ferritin (Fe3) forms. However, its unusual increment manifests iron overload, which reacts with hydrogen peroxide. This reaction will generate hydroxyl radicals, and irons higher oxidation states. Further, this reaction causes tissue damage or cognitive decline in the brain and also leads to neurodegenerative diseases. The susceptibility difference due to iron overload within the volume of interest (VOI) responsible for field perturbation of MRI and can benefit in estimating the neural disorder. The quantitative susceptibility mapping (QSM) technique can estimate susceptibility alteration and assist in quantifying the local tissue susceptibility differences. It has attracted many researchers and clinicians to diagnose and detect neural disorders such as Parkinsons, Alzheimers, Multiple Sclerosis, and aging. The paper presents a systematic review illustrating QSM fundamentals and its processing steps, including phase unwrapping, background field removal, and susceptibility inversion. Using QSM, the present work delivers novel predictive biomarkers for various neural disorders. It can strengthen new researchers fundamental knowledge and provides insight into its applicability for cognitive decline disclosure. The paper discusses the future scope of QSM processing stages and their applications in identifying new biomarkers for neural disorders

Deep generative modelling of the imaged human brain

Human-machine symbiosis is a very promising opportunity for the field of neurology given that the interpretation of the imaged human brain is a trivial feat for neither entity. However, before machine learning systems can be used in real world clinical situations, many issues with automated analysis must first be solved. In this thesis I aim to address what I consider the three biggest hurdles to the adoption of automated machine learning interpretative systems. For each issue, I will first elucidate the reader on its importance given the overarching narratives of both neurology and machine learning, and then showcase my proposed solutions to these issues through the use of deep generative models of the imaged human brain. First, I start by addressing what is an uncontroversial and universal sign of intelligence: the ability to extrapolate knowledge to unseen cases. Human neuroradiologists have studied the anatomy of the healthy brain and can therefore, with some success, identify most pathologies present on an imaged brain, even without having ever been previously exposed to them. Current discriminative machine learning systems require vast amounts of labelled data in order to accurately identify diseases. In this first part I provide a generative framework that permits machine learning models to more efficiently leverage unlabelled data for better diagnoses with either none or small amounts of labels. Secondly, I address a major ethical concern in medicine: equitable evaluation of all patients, regardless of demographics or other identifying characteristics. This is, unfortunately, something that even human practitioners fail at, making the matter ever more pressing: unaddressed biases in data will become biases in the models. To address this concern I suggest a framework through which a generative model synthesises demographically counterfactual brain imaging to successfully reduce the proliferation of demographic biases in discriminative models. Finally, I tackle the challenge of spatial anatomical inference, a task at the centre of the field of lesion-deficit mapping, which given brain lesions and associated cognitive deficits attempts to discover the true functional anatomy of the brain. I provide a new Bayesian generative framework and implementation that allows for greatly improved results on this challenge, hopefully, paving part of the road towards a greater and more complete understanding of the human brain