Bio-inspired Physiological Signal(s) and Medical Image(s) Neural Processing Systems Based on Deep Learning and Mathematical Modeling for Implementing Bio-Engineering Applications in Medical and Industrial Fields

Cellular-resolution connectomics is an ambitious research direction with the goal of generating comprehensive brain connectivity maps using high-throughput, nano-scale electron microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Deep learning has provided exceptional performance in image classification tasks in computer vision, leading to a recent explosion in popularity. Similarly, its application to connectomic analyses holds great promise. Here, we introduce a deep neural network architecture, FusionNet, with a focus on its application to accomplish automatic segmentation of neuronal structures in connectomics data. FusionNet combines recent advances in machine learning, such as semantic segmentation and residual neural networks, with summation-based skip connections. This results in a much deeper network architecture and improves segmentation accuracy. We demonstrate the performance of the proposed method by comparing it with several other popular electron microscopy segmentation methods. We further illustrate its flexibility through segmentation results for two different tasks: cell membrane segmentation and cell nucleus segmentation.

The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases.