Mathematical Imaging and Surface Processing

Within the last decade image and geometry processing have become increasingly rigorous with solid foundations in mathematics. Both areas are research fields at the intersection of different mathematical disciplines, ranging from geometry and calculus of variations to PDE analysis and numerical analysis. The workshop brought together scientists from all these areas and a fruitful interplay took place. There was a lively exchange of ideas between geometry and image processing applications areas, characterized in a number of ways in this workshop. For example, optimal transport, first applied in computer vision is now used to define a distance measure between 3d shapes, spectral analysis as a tool in image processing can be applied in surface classification and matching, and so on. We have also seen the use of Riemannian geometry as a powerful tool to improve the analysis of multivalued images. This volume collects the abstracts for all the presentations covering this wide spectrum of tools and application domains

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

Image Quality Improvement of Medical Images using Deep Learning for Computer-aided Diagnosis

Retina image analysis is an important screening tool for early detection of multiple dis eases such as diabetic retinopathy which greatly impairs visual function. Image analy sis and pathology detection can be accomplished both by ophthalmologists and by the use of computer-aided diagnosis systems. Advancements in hardware technology led to more portable and less expensive imaging devices for medical image acquisition. This promotes large scale remote diagnosis by clinicians as well as the implementation of computer-aided diagnosis systems for local routine disease screening. However, lower cost equipment generally results in inferior quality images. This may jeopardize the reliability of the acquired images and thus hinder the overall performance of the diagnos tic tool. To solve this open challenge, we carried out an in-depth study on using different deep learning-based frameworks for improving retina image quality while maintaining the underlying morphological information for the diagnosis. Our results demonstrate that using a Cycle Generative Adversarial Network for unpaired image-to-image trans lation leads to successful transformations of retina images from a low- to a high-quality domain. The visual evidence of this improvement was quantitatively affirmed by the two proposed validation methods. The first used a retina image quality classifier to confirm a significant prediction label shift towards a quality enhance. On average, a 50% increase of images being classified as high-quality was verified. The second analysed the perfor mance modifications of a diabetic retinopathy detection algorithm upon being trained with the quality-improved images. The latter led to strong evidence that the proposed solution satisfies the requirement of maintaining the images’ original information for diagnosis, and that it assures a pathology-assessment more sensitive to the presence of pathological signs. These experimental results confirm the potential effectiveness of our solution in improving retina image quality for diagnosis. Along with the addressed con tributions, we analysed how the construction of the data sets representing the low-quality domain impacts the quality translation efficiency. Our findings suggest that by tackling the problem more selectively, that is, constructing data sets more homogeneous in terms of their image defects, we can obtain more accentuated quality transformations

Radiomics and machine learning methods for 2-year overall survival prediction in non-small cell lung cancer patients

In recent years, the application of radiomics to lung cancer show encouraging results in the prediction of histological outcomes, survival times, staging of the disease and so on. In this thesis, radiomics and deep learning applications are compared by analyzing their performance in the prediction of the 2-year overall survival (OS) in patients affected by non-small cell lung cancer (NSCLC). The dataset under exam contains 417 patients with both clinical data and computed tomography (CT) examinations of the chest. Radiomics extracts handcrafted radiomic features from the three-dimensional tumor region of interest (ROI). It is the approach that better predicts the 2-year overall survival with a training and test area under the receiver operating characteristic curve (AUC) equal to 0.683 and 0.652. Concerning deep learning applications, two methods are considered in this thesis: deep features and convolutional neural networks (CNN). The first method is similar to radiomics, but substitutes handcrafted features with deep features extracted from the bi-dimensional slices that build the three-dimensional tumor ROI. In particular, two different main classes of deep features are considered: the latent variables returned by a convolutional autoencoder (CAE) and the inner features learnt by a pre-trained CNN. The results for latent variables returned by CAE show an AUC of 0.692 in training set and 0.631 in test set. The second method considers the direct classification of the CT images themselves by means of CNN. They perform better than deep features and they reach an AUC equal to 0.692 in training set and 0.644 in test set. For CNN, the impact of using generative adversarial networks (GAN) to increase the dataset dimension is also investigated. This analysis results in poorly defined images, where the synthesis of the bones is incompatible with the actual structure of the tumor mass. In general, deep learning applications perform worse than radiomics, both in terms of lower AUC and greater generalization gap between training and test sets. The main issue encountered in their training is the limited number of patients that is responsible for overfitting on CNN, inacurrate reconstructions on CAE and poor synthetic images on GAN. This limit is reflected in the necessity to reduce the complexity of the models by implementing a two-dimensional analysis of the tumor masses, in contrast with the three-dimensional study performed by radiomics. However, the bi-dimensional restriction is responsible for an incomplete description of the tumor masses, reducing the predictive capabilities of deep learning applications. In summary, our analysis, spanning a wide set of more than 7000 combinations, shows that with the current dataset it is only possible to match the performances of previous works. This detailed survey suggests that we have reached the state of the art in terms of analysis and that more data are needed to improve the predictions

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

Diagnosis of Neurodegenerative Diseases using Deep Learning

Automated disease classification systems can assist radiologists by reducing workload while initiating therapy to slow disease progression and improve patients’ quality of life. With significant advances in machine learning (ML) and medical scanning over the last decade, medical image analysis has experienced a paradigm change. Deep learning (DL) employing magnetic resonance imaging (MRI) has become a prominent method for computer-assisted systems because of its ability to extract high-level features via local connection, weight sharing, and spatial invariance. Nonetheless, there are several important research challenges when advancing toward clinical application, and these problems inspire the contributions presented throughout this thesis. This research develops a framework for the classification of neurodegenerative diseases using DL techniques and MRI. The presented thesis involves three evolution stages. The first stage is the development of a robust and reproducible 2D classification system with high generalisation performance for Alzheimer’s disease (AD), mild cognitive impairment (MCI), and Parkinson’s disease (PD) using deep convolutional neural networks (CNN). The next phase of the first stage extends this framework and demonstrates its use on different datasets while quantifying the effect of a highly observed phenomenon called data leakage in the literature. Key contributions of the thesis presented in this stage are a thorough analysis of the literature, a discussion on the potential flaws of the selected studies, and the development of an open-source evaluation system for neurodegenerative disease classification using structural MRI. The second stage aims to overcome the problems stem from investigating 3D data with 2D models. With this goal, a 3D CNN-based diagnostic framework is developed for classifying AD and PD patients from healthy controls using T1-weighted brain MRI data. The last stage includes two phases with a focus on AD and MCI diagnosis. The first phase proposes a new autoencoder-based deep neural network structure by integrating supervised prediction and unsupervised representation. The second phase introduces the final contribution of the thesis which is a novel ensemble approach that may also be used to predict diseases other than neurodegenerative ones (e.g., tuberculosis (TB)) using a modality apart from MRI

Developments of Advanced Cathodes and Stabilized Zinc Anodes for High-performance Aqueous Zinc-ion Batteries

Aqueous rechargeable zinc-ion batteries (ZIBs) have attracted considerable attention as one of the most promising energy storage systems for the grid-scale application owing to the natural merits of metallic Zn, including a high theoretical capacity, suitable redox potential, low cost, high safety, and eco-friendliness. However, the existing aqueous ZIBs are far from satisfying the requirements of practical applications. Significant challenges hindering the further development of ZIBs come from the low utilization and poor cycling stability of cathodes and limited reversibility of Zn anodes associated with dendrite growth, corrosion, and passivation. To date, enormous efforts have been devoted to developing high-performance cathode materials, reliable electrolytes, and stable Zn anodes to achieve ZIB with high energy and power densities and long cycle life. These progresses have been reviewed in this dissertation. Regarding the main issues of ZIBs, the dissertation covered both the cathode and anode to comprehensively improve the electrochemical performance of ZIBs. For the cathode, high-performance manganese oxide-based cathode materials have been developed by in-situ electrochemical activation of MnS, and rational design of hierarchical core-shell MnO2@carbon nanofiber structures. To further understand the underlying reasons for the enhanced electrochemical performance, the charge storage mechanisms of manganese oxide-based cathodes in ZIBs have been in-depth investigated. With respect to the Zn anode, a thin polyvinyl alcohol (PVA) coating layer on the Zn anode has enabled dendrite-free, long-life aqueous Zn batteries by effectively regulating the interfacial ion diffusion and inducing the homogeneous Zn nucleation and deposition of stacked plates with preferentially crystallographic orientation along (002)Zn planes. This work is expected to provide facile and low-cost approaches for developing high-performance, cost-effective, and stable aqueous ZIBs and shed light on a new mechanistic understanding of manganese oxide-based cathodes

Spatio-temporal modelling of tornados with R-INLA, at the county-level in Texas and Ocklahona

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe United States of America is the county in the world that is more prone to tornado occurrence. This fact led many researchers, for the past years, to study and formulate theories about tornado occurrence, and which factors promote tornadogenesis. The theories around tornados are always coupled with an attempt to predict their occurrence, for better disaster alertness, and response, in case they happen. At the country level, the tornado occurrence is highly studied and understood. But the same does not happen for the state level, or county level. In this thesis, it is proposed a statistical model to characterize the occurrence of tornados in a state, given physical (terrain roughness and land-cover types)and demographic properties of its counties. This model also takes into consideration the spatial and temporal dimensions, as well as a space time interaction component. This model was applied for Oklahoma and Texas. The model with the covariates fits Texas‟ tornado occurrence, but for Oklahoma, only the spatio-temporal formulation can be applied. For Texas, the model explains the covariates as being congruent with the low-level inflow hypothesis, with tornados decreasing in zones where natural barriers for the flow can be constituted. Under the Bayesian framework, maps of spatial risk and probability of tornado occurrence for Texas and Oklahoma were computed, that can be used to make predictions in the future