Modeling and Analysis of Subcellular Protein Localization in Hyper-Dimensional Fluorescent Microscopy Images Using Deep Learning Methods

Hyper-dimensional images are informative and become increasingly common in biomedical research. However, the machine learning methods of studying and processing the hyper-dimensional images are underdeveloped. Most of the methods only model the mapping functions between input and output by focusing on the spatial relationship, whereas neglect the temporal and causal relationships. In many cases, the spatial, temporal, and causal relationships are correlated and become a relationship complex. Therefore, only modeling the spatial relationship may result in inaccurate mapping function modeling and lead to undesired output. Despite the importance, there are multiple challenges on modeling the relationship complex, including the model complexity and the data availability. The objective of this dissertation is to comprehensively study the mapping function modeling of the spatial-temporal and the spatial-temporal-causal relationship in hyper-dimensional data with deep learning approaches. The modeling methods are expected to accurately capture the complex relationships in class-level and object-level so that new image processing tools can be developed based on the methods to study the relationships between targets in hyper-dimensional data. In this dissertation, four different cases of relationship complex are studied, including the class-level spatial-temporal-causal relationship and spatial-temporal relationship modeling, and the object-level spatial-temporal-causal relationship and spatial-temporal relationship modeling. The modelings are achieved by deep learning networks that implicitly model the mapping functions with network weight matrix. For spatial-temporal relationship, because the cause factor information is unavailable, discriminative modeling that only relies on available information is studied. For class-level and object-level spatial-temporal-causal relationship, generative modeling is studied with a new deep learning network and three new tools proposed. For spatial-temporal relationship modeling, a state-of-the-art segmentation network has been found to be the best performer over 18 networks. Based on accurate segmentation, we study the object-level temporal dynamics and interactions through dynamics tracking. The multi-object portion tracking (MOPT) method allows object tracking in subcellular level and identifies object events, including object born, dead, split, and fusion. The tracking results is 2.96% higher on consistent tracking accuracy and 35.48% higher on event identification accuracy, compared with the existing state-of-the-art tracking methods. For spatial-temporal-causal relationship modeling, the proposed four-dimensional reslicing generative adversarial network (4DR-GAN) captures the complex relationships between the input and the target proteins. The experimental results on four groups of proteins demonstrate the efficacy of 4DR-GAN compared with the widely used Pix2Pix network. On protein localization prediction (PLP), the predicted localization from 4DR-GAN is more accurate in subcellular localization, temporal consistency, and dynamics. Based on efficient PLP, the digital activation (DA) and digital inactivation (DI) tools allow precise spatial and temporal control on global and local localization manipulation. They allow researchers to study the protein functions and causal relationships by observing the digital manipulation and PLP output response

Motion Analysis of Live Objects by Super-Resolution Fluorescence Microscopy

Motion analysis plays an important role in studing activities or behaviors of live objects in medicine, biotechnology, chemistry, physics, spectroscopy, nanotechnology, enzymology, and biological engineering. This paper briefly reviews the developments in this area mostly in the recent three years, especially for cellular analysis in fluorescence microscopy. The topic has received much attention with the increasing demands in biomedical applications. The tasks of motion analysis include detection and tracking of objects, as well as analysis of motion behavior, living activity, events, motion statistics, and so forth. In the last decades, hundreds of papers have been published in this research topic. They cover a wide area, such as investigation of cell, cancer, virus, sperm, microbe, karyogram, and so forth. These contributions are summarized in this review. Developed methods and practical examples are also introduced. The review is useful to people in the related field for easy referral of the state of the art

THE ROLE OF VASCULAR ENDOTHELIAL GROWTH FACTOR IN LEUKEMIA TRAFFICKING

Vascular endothelial growth factor (VEGF) is a signaling protein involved in inducing and regulating endothelial cell proliferation and function (Duffy et al 2000). VEGF is also involved in cancer progression, as it induces vascular permeability and promotes angiogenesis to tumor laden areas, giving cancer cells critical oxygen and nutrients (Hoeppner et al.,2012. Studies indicate VEGF prevents lymphoblast apoptosis, which may contribute to leukemia formation and enable the proliferation of leukemic cells (Duffy et al 2000). Ongoing research seeks to further examine VEGF in leukemia, using a rag2:GFP-Myc expressing transgenic zebrafish as the animal model of T-cell Acute Lymphoblastic Leukemia (T-ALL). Recent findings have concluded a relationship between VEGF expression in leukemic fish remodels the microenvironment leading to cell migration, but not through vascular restructuring, as a means to upregulate leukemic expression

Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging

Cameras were originally designed using physics-based heuristics to capture aesthetic images. In recent years, there has been a transformation in camera design from being purely physics-driven to increasingly data-driven and task-specific. In this paper, we present a framework to understand the building blocks of this nascent field of end-to-end design of camera hardware and algorithms. As part of this framework, we show how methods that exploit both physics and data have become prevalent in imaging and computer vision, underscoring a key trend that will continue to dominate the future of task-specific camera design. Finally, we share current barriers to progress in end-to-end design, and hypothesize how these barriers can be overcome

Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions

Technical implementations of light sheet microscopy

Fluorescence-based microscopy is among the most successful methods in biological studies. It played a critical role in the visualization of subcellular structures and in the analysis of complex cellular processes, and it is nowadays commonly employed in genetic and drug screenings. Among the fluorescence-based microscopy techniques, light sheet fluorescence microscopy (LSFM) has shown a quite interesting set of benefits. The technique combines the speed of epi-fluorescence acquisition with the optical sectioning capability typical of confocal microscopes. Its unique configuration allows the excitation of only a thin plane of the sample, thus fast, high resolution imaging deep inside tissues is nowadays achievable. The low peak intensity with which the sample is illuminated diminishes phototoxic effects and decreases photobleaching of fluorophores, ensuring data collection for days with minimal adverse consequences on the sample. It is no surprise that LSFM applications have raised in just few years and the technique has been applied to study a wide variety of samples, from whole organism, to tissues, to cell clusters, and single cells. As a consequence, in recent years numerous set-ups have been developed, each one optimized for the type of sample in use and the requirements of the question at hand. Hereby, we aim to review the most advanced LSFM implementations to assist new LSFM users in the choice of the LSFM set-up that suits their needs best. We also focus on new commercial microscopes and do-it-yourself strategies; likewise we review recent designs that allow a swift integration of LSFM on existing microscopes

3D + t Morphological Processing: Applications to Embryogenesis Image Analysis

We propose to directly process 3D + t image sequences with mathematical morphology operators, using a new classi?cation of the 3D+t structuring elements. Several methods (?ltering, tracking, segmentation) dedicated to the analysis of 3D + t datasets of zebra?sh embryogenesis are introduced and validated through a synthetic dataset. Then, we illustrate the application of these methods to the analysis of datasets of zebra?sh early development acquired with various microscopy techniques. This processing paradigm produces spatio-temporal coherent results as it bene?ts from the intrinsic redundancy of the temporal dimension, and minimizes the needs for human intervention in semi-automatic algorithms

Bioimage informatics in STED super-resolution microscopy

Optical microscopy is living its renaissance. The diffraction limit, although still physically true, plays a minor role in the achievable resolution in far-field fluorescence microscopy. Super-resolution techniques enable fluorescence microscopy at nearly molecular resolution. Modern (super-resolution) microscopy methods rely strongly on software. Software tools are needed all the way from data acquisition, data storage, image reconstruction, restoration and alignment, to quantitative image analysis and image visualization. These tools play a key role in all aspects of microscopy today – and their importance in the coming years is certainly going to increase, when microscopy little-by-little transitions from single cells into more complex and even living model systems. In this thesis, a series of bioimage informatics software tools are introduced for STED super-resolution microscopy. Tomographic reconstruction software, coupled with a novel image acquisition method STED< is shown to enable axial (3D) super-resolution imaging in a standard 2D-STED microscope. Software tools are introduced for STED super-resolution correlative imaging with transmission electron microscopes or atomic force microscopes. A novel method for automatically ranking image quality within microscope image datasets is introduced, and it is utilized to for example select the best images in a STED microscope image dataset.Siirretty Doriast