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Health is a fundamental human right. To increase global health, research in the medical sector is of great importance. Decreasing time consumption of biomedical testing could accelerate the research and development of new drugs and vaccines. This could be achieved by automation of biomedical analysis, using computerized methods. In order to perform analysis on pictures of biomedical tests, it is important to identify the area of interest (AOI) of the test. For example, cells and bacteria are commonly grown in petri dishes, in this case the AOI is the bottom area of the dish, since this is where the object of analysis is located.This study was performed with the aim to compare a few computerized methods for identifying the AOI in pictures of biomedical tests. In the study, biomedical images from a testing method called ELISpot have been used. ELISpot uses plates with up to 96 circular wells, where pictures of the separate wells were used in order to find the AOI corresponding to the bottom area of each well. The focus has been on comparing the performance of three edge detection methods. More specifically, their ability to accurately detect the edges of the well. Furthermore, a method for identifying a circle based on the detected edges was used to specify the AOI.The study shows that methods using second order derivatives for edge detection, gives the best results regarding to robustness.

<p>  </p> <p>Cardiovascular disease has been the leading cause of death in the United States for over 70 years. To evaluate the extent and progression of cardiovascular disease, non-invasive imaging techniques are frequently used clinically and pre-clinically. Current echocardiographic and cine magnetic resonance approaches rely on measurements that are typically obtained from two-dimensional images, which assumes uniformity of the structure being evaluated. To explore methods to potentially address these shortcomings, our group has developed and validated high frequency four-dimensional ultrasound techniques as well as created a software toolbox that allows for measurement of myocardial kinematics. In this thesis, I assisted in the application of these methods to two murine models of disease states: myocardial infarction and aortic aneurysm. Another study I aided in focused on cardiac magnetic resonance imaging data from patients with Duchenne muscular dystrophy. From our software, we are able to obtain various strain and strain rate estimates that reveal significant functional changes in infarction and Duchenne muscular dystrophy earlier than standard measurement techniques. Furthermore, we are able to identify vascular expansion, transmural thickening, and changes in hemodynamics prior to aneurysm development. Earlier detection and localization allows for more targeted surveillance and interventions, which ultimately may result in improved clinical outcomes. Ideally, these findings can be used to expand the capabilities of cardiac research and the development of clinically applicable imaging techniques and treatments to better address underlying cardiovascular pathophysiology. </p>

<p>This dissertation details the creation, validation, and characterization of a porcine ex-vivo culture model to study subcuteneous tissue. The viability of the model was assessed over seven days of culture by digestion and the proliferation and death of cells was monitored by immunohistochmeical labelling and image analysis. The model was then used in a timecourse proteomics experiment to characterize the effect of culture on subcutaneous proteome. The model was then compared to a commercially available human ex-vivo model with respect to viability and changes to the subcutaneous proteome. </p>

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<p dir="ltr">Progress in deep learning methodologies has transformed the landscape of medical image analysis, opening fresh pathways for precise and effective diagnostics. Currently, multi-phase liver CT scans follow a four-stage process, commencing with an initial scan carried out before the administration of <a href="" target="_blank">intravenous (IV) contrast-enhancing material</a>. Subsequently, three additional scans are performed following the contrast injection. The primary objective of this research is to automate the analysis and prediction of 50% of liver CT scans. It concentrates on discerning patterns of intensity change during the second, third, and fourth phases concerning the initial phase. The thesis comprises two key sections. The first section employs the non-contrast phase (first scan), late hepatic arterial phase (second scan), and portal venous phase (third scan) to predict the delayed phase (fourth scan). In the second section, the non-contrast phase and late hepatic arterial phase are utilized to predict both the portal venous and delayed phases. The study evaluates the performance of two deep learning models, U-Net and U²-Net. The process involves preprocessing steps like subtraction and normalization to compute contrast difference images, followed by post-processing techniques to generate the predicted 2D CT scans. Post-processing steps have similar techniques as in preprocessing but are performed in reverse order. Four fundamental evaluation metrics, including <a href="" target="_blank">Mean Absolute Error (MAE), Signal-to-Reconstruction Error Ratio (SRE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), </a>are employed for assessment. Based on these evaluation metrics, U²-Net performed better than U-Net for the prediction of both portal venous (third) and delayed (fourth) phases. Specifically, U²-Net exhibited superior MAE and PSNR results for the predicted third and fourth scans. However, U-Net did show slightly better SRE and SSIM performance in the predicted scans. On the other hand, for the exclusive prediction of the fourth scan, U-Net outperforms U²-Net in all four evaluation metrics. This implementation shows promising results which will eliminate the need for additional CT scans and reduce patients’ exposure to harmful radiation. Predicting 50% of liver CT volumes will reduce exposure to harmful radiation by half. The proposed method is not limited to liver CT scans and can be applied to various other multi-phase medical imaging techniques, including multi-phase CT angiography, multi-phase renal CT, contrast-enhanced breast MRI, and more.</p>

<p>Biotherapeutics, such as monoclonal antibodies (mAbs), represent a primary mechanism for treatment of human disease, and there has been a steady increase in Food and Drug Administration approvals since the first one in 1982. Subcutaneous (SC) injection of protein-based therapeutics is a convenient and clinically established drug delivery method that increases the convenience and reduces cost compared to other delivery methods. However, progress is needed to optimize bioavailability via this route. This dissertation describes the methods for evaluation of mass transport of high molecular weight proteins, such as mAbs, following SC injection using <em>in vitro</em> and <em>ex vivo</em> modeling developed to describe the factors relevant for optimal distribution prior to uptake into systemic circulation. The first chapter describes a novel collagen and hyaluronic acid (HA) based hydrogel for investigation of macromolecule transport based on the physiochemical properties of the diffusing molecule and the tissue matrix. This initial study demonstrated that, in collagen alone, collagen combined with HA, and HA alone, the molecules demonstrated different transport paradigms dependent primarily on molecule size, matrix viscosity, and electrostatic charge, respectively. This showed that the local tissue heterogeneity and therapeutic properties could be determining factors for molecule transport and bioavailability. The second, third, and fourth chapters describe two novel platforms for the investigation of injection plume formation in SC tissue utilizing three-dimensional X-ray tomography. Injection plume analysis has been studied comprehensively in the context of insulin transport using co-injection of radiopaque dyes to track the protein distribution. However high molecular weight therapeutics have vastly different physiochemical properties than insulin and are injected under different rates, concentrations, volumes, and viscosities due to dosing considerations. To address the gap mAb distribution, we first developed a novel protein conjugated to an x-ray contrast agent to directly track injection plume formation and investigated the effects of injection rate and tissue location through injections into ex vivo porcine tissue, described in chapters three and four. Ex vivo tissue analysis showed that the rate did not influence the distribution, however, plume volume was lower in porcine belly compared to neck tissue. Whereas porcine tissue is an excellent model to represent the structural features of human injection, the large heterogeneity between animal subjects and collected samples is a disadvantage. Therefore, the fourth chapter describes the fabrication of a gelatin hydrogel-based injection platform representing the dermal and subcutaneous tissue layers for controlled injection plume analysis. In summary, all three models represent useful platforms for the assessment of macromolecular mass transport, pharmaceutical autoinjector performance, as well as the potential impact of tissue properties and intersubject heterogeneity on plume formation. Overall, the findings in these studies might better inform drug designers and clinicians on how to most optimally engineer an injection to deliver the most efficient patient outcomes through better dosing and increased cost savings. </p>

<p dir="ltr">This thesis describes advancements in both chemical imaging and optical manipulation methodologies for their application in tandem monitoring and control of biochemical processes. We developed a fast acquisition multimodal nonlinear imaging platform based on pulse-picking to minimize photoperturbation to the sample during imaging. By frequency doubling the imaging source, through acousto-optic modulation and simple comparator circuitry, we developed a comprehensive platform that uses chemical specific signals generated during imaging to control the pixel location for laser activation for reaction control. This feedback loop allows for advanced decision logic on a pixel by pixel basis.</p>

<p dir="ltr">In this dissertation, efforts and innovations are made to advance subtle pattern mining, edge computing, and system efficiency optimization for biomedical applications, thereby advancing precision medicine big data.</p><p dir="ltr">Brain visual dynamics encode rich functional and biological patterns of the neural system, promising for applications like intention decoding, cognitive load quantization and neural disorder measurement. We here focus on the understanding of the brain visual dynamics for the Amyotrophic lateral sclerosis (ALS) population. We leverage a deep learning framework for automatic feature learning and classification, which can translate the eye Electrooculography (EOG) signal to meaningful words. We then build an edge computing platform on the smart phone, for learning, visualization, and decoded word demonstration, all in real-time. In a further study, we have leveraged deep transfer learning to boost EOG decoding effectiveness. More specifically, the model trained on basic eye movements is leveraged and treated as an additional feature extractor when classifying the signal to the meaningful word, resulting in higher accuracy.</p><p dir="ltr">Efforts are further made to decoding functional Near-Infrared Spectroscopy (fNIRS) signal, which encodes rich brain dynamics like the cognitive load. We have proposed a novel Multi-view Multi-channel Graph Neural Network (mmGNN). More specifically, we propose to mine the multi-channel fNIRS dynamics with a multi-stage GNN that can effectively extract the channel- specific patterns, propagate patterns among channels, and fuse patterns for high-level abstraction. Further, we boost the learning capability with multi-view learning to mine pertinent patterns in temporal, spectral, time-frequency, and statistical domains.</p><p dir="ltr">Massive-device systems, like wearable massive-sensor computers and Internet of Things (IoTs), are promising in the era of big data. The crucial challenge is about how to maximize the efficiency under coupling constraints like energy budget, computing, and communication. We propose a deep reinforcement learning framework, with a pattern booster and a learning adaptor. This framework has demonstrated optimally maximizes the energy utilization and computing efficiency on the local massive devices under a one-center fifteen-device circumstance.</p><p dir="ltr">Our research and findings are expected to greatly advance the intelligent, real-time, and efficient big data harnessing, leveraging deep learning, edge computing, and efficiency optimization.</p>

<p dir="ltr">Single-particle tracking (SPT) provides high-resolution spatial-temporal information on biomolecule dynamics. However, localization inaccuracies, limited track lengths, heterogeneous fluorescence backgrounds, and potential molecular motion blur pose significant challenges that hinder the accurate extraction of movement trajectories and their underlying motion behavior. The conventional SPT pipeline struggles to comprehensively address detection, localization, linkage, and parameter inference simultaneously, resulting in information loss during sequential processing. To overcome these challenges, we propose SPTnet, an end-to-end deep learning framework that leverages a transformer-based architecture to optimize trajectory and motion parameter estimations in parallel through a global loss. SPTnet bypasses traditional SPT processes, directly inferring molecular trajectories and motion parameters from fluorescence microscopy video frames with precision approaching the statistical information limit. Our results demonstrate that SPTnet outperforms conventional methods under commonly encountered but challenging conditions such as short trajectories, low signal-to-noise ratio (SNR), heterogeneous backgrounds, motion blur, and especially when molecules exhibit non-Brownian behaviors.</p><p dir="ltr">Besides SPT, we used single-molecule localization microscopy (SMLM) to study cytokinetic protein in fission yeast. During cytokinesis, myosin-II constricts the contractile ring that separates one cell into two daughter cells. The fission yeast cytokinetic contractile ring contains two types of myosin Ⅱ, Myo2 and Myp2. However, the precise ultrastructural arrangement of the two type Ⅱ myosins remains in question. We investigated the relative spatial arrangement of Myo2p and Myp2p within contractile ring using two-color super-resolution microscopy based on salvaged fluorescence imaging. Quantitative analysis of the nanoscale images should provide useful information for modeling contractile ring assembly and constriction.</p><p><br></p>