Deep multi-modal U-net fusion methodology of infrared and ultrasonic images for porosity detection in additive manufacturing

We developed a deep fusion methodology of non-destructive (NDT) in-situ infrared and ex- situ ultrasonic images for localization of porosity detection without compromising the integrity of printed components that aims to improve the Laser-based additive manufacturing (LBAM) process. A core challenge with LBAM is that lack of fusion between successive layers of printed metal can lead to porosity and abnormalities in the printed component. We developed a sensor fusion U-Net methodology that fills the gap in fusing in-situ thermal images with ex-situ ultrasonic images by employing a U-Net Convolutional Neural Network (CNN) for feature extraction and two-dimensional object localization. We modify the U-Net framework with the inception and LSTM block layers. We validate the models by comparing our single modality models and fusion models with ground truth X-ray computed tomography images. The inception U-Net fusion model localized porosity with the highest mean intersection over union score of 0.557

Hybrid optical and magnetic manipulation of microrobots

Microrobotic systems have the potential to provide precise manipulation on cellular level for diagnostics, drug delivery and surgical interventions. These systems vary from tethered to untethered microrobots with sizes below a micrometer to a few microns. However, their main disadvantage is that they do not have the same capabilities in terms of degrees-of-freedom, sensing and control as macroscale robotic systems. In particular, their lack of on-board sensing for pose or force feedback, their control methods and interface for automated or manual user control are limited as well as their geometry has few degrees-of-freedom making three-dimensional manipulation more challenging. This PhD project is on the development of a micromanipulation framework that can be used for single cell analysis using the Optical Tweezers as well as a combination of optical trapping and magnetic actuation for recon gurable microassembly. The focus is on untethered microrobots with sizes up to a few tens of microns that can be used in enclosed environments for ex vivo and in vitro medical applications. The work presented investigates the following aspects of microrobots for single cell analysis: i) The microfabrication procedure and design considerations that are taken into account in order to fabricate components for three-dimensional micromanipulation and microassembly, ii) vision-based methods to provide 6-degree-offreedom position and orientation feedback which is essential for closed-loop control, iii) manual and shared control manipulation methodologies that take into account the user input for multiple microrobot or three-dimensional microstructure manipulation and iv) a methodology for recon gurable microassembly combining the Optical Tweezers with magnetic actuation into a hybrid method of actuation for microassembly.Open Acces

Deep learning‐based method for reducing residual motion effects in diffusion parameter estimation

PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects. METHODS: The data-rejection approach was adopted in which motion-corrupted data are discarded before model-fitting. A deep learning-based parameter estimation algorithm, using a hierarchical convolutional neural network (H-CNN), was combined with motion assessment and corrupted volume rejection. The method was designed to overcome the limitations of existing methods of this kind that produce parameter estimations whose quality depends strongly on a proportion of the data discarded. Evaluation experiments were conducted for the estimation of diffusion kurtosis and diffusion-tensor-derived measures at both the individual and group levels. The performance was compared with the robust approach of iteratively reweighted linear least squares (IRLLS) after motion correction with and without outlier replacement. RESULTS: Compared with IRLLS, the H-CNN-based technique is minimally sensitive to motion effects. It was tested at severe motion levels when 70% to 90% of the data are rejected and when random motion is present. The technique had a stable performance independent of the numbers and schemes of data rejection. A further test on a data set from children with attention-deficit hyperactivity disorder shows the technique can potentially ameliorate spurious group-level difference caused by head motion. CONCLUSION: This method shows great potential for reducing residual motion effects in motion-corrupted diffusion-weighted-imaging data, bringing benefits that include reduced bias in derived metrics in individual scans and reduced motion-level-dependent bias in population studies employing diffusion MRI

Machine Learning for Biomedical Application

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches in each task are analyzed with regard to what governing physical processes are modeled, formulated and how they are incorporated, i.e. modify data (observation bias), modify networks (inductive bias), and modify losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency and generalization in increasingly realistic applications

Big data analysis of cyclic alternating pattern during sleep using deep learning

Sleep scoring has been of great interest since the invention of the polysomnography method, which enabled the recording of physiological signals overnight. With the surge in wearable devices in recent years, the topic of what is high-quality sleep, how can it be determined and how can it be achieved attracted increasing interest. In the last two decades, cyclic alternating pattern (CAP) was introduced as a scoring alternative to traditional sleep staging. CAP is known as a synonym for sleep microstructure and describes sleep instability. Manual CAP scoring performed by sleep experts is a very exhausting and time-consuming task. Hence, an automatic method would facilitate the processing of sleep data and provide a valuable tool to enhance the understanding of the role of CAP. This thesis aims to expand the knowledge about CAP by developing a high-performance automated CAP scoring system that can reliably detect and classify CAP events in sleep recordings. The automated system is equipped with state-of-the-art signal processing methods and exploits the dynamic, temporal information in brain activity using deep learning. The automated scoring system is validated using large community-based cohort studies and comparing the output to verified values in the literature. Our findings present novel clinical results on the relationship between CAP and age, gender, subjective sleep quality, and sleep disorders demonstrating that automated CAP analysis of large population based studies can lead to new findings on CAP and its subcomponents. Next, we study the relationship between CAP and behavioural, cognitive, and quality-of-life measures and the effect of adenotonsillectomy on CAP in children with obstructive sleep apnoea as the link between CAP and cognitive functioning in children is largely unknown. Finally, we investigate cortical-cardiovascular interactions during CAP to gain novel insights into the causal relationships between cortical and cardiovascular activity that are underpinning the microstructure of sleep. In summary, the research outcomes in this thesis outline the importance of a fully automated end-to-end CAP scoring solution for future studies on sleep microstructure. Furthermore, we present novel critical information for a better understanding of CAP and obtain first evidence on physiological network dynamics between the central nervous system and the cardiovascular system during CAP.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202