Browsing by Author "Polys, Nicholas Fearing"

Now showing 1 - 3 of 3
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    Deep Learning Approach for Cell Nuclear Pore Detection and Quantification over High Resolution 3D Data
    He, Chongyu (Virginia Tech, 2023-12-21)
    The intricate task of segmenting and quantifying cell nuclear pores in high-resolution 3D microscopy data is critical for cellular biology and disease research. This thesis introduces a deep learning pipeline crafted to automate the segmentation and quantification of nuclear pores from high-resolution 3D cell organelle images. Our aim is to refine computational methods capable of handling the data's complexity and size, thus improving accuracy and reducing manual labor in biological image analysis. The developed pipeline incorporates data preprocessing, augmentation strategies, random block sampling, and a three-stage post-processing algorithm. It utilizes a 3D U-Net with a VGG-16 backbone, optimized through cyclical data augmentation and random block sampling to tackle the challenges posed by limited labeled data and the processing of large-scale 3D images. The pipeline has demonstrated its capability to effectively learn and predict nuclear pore structures, achieving improvements in validation metrics compared to baseline models. Our experiments suggest that cyclical augmentation helps prevent overfitting, and random block sampling contributes to managing data imbalance. The post-processing phase successfully automates the quantification of nuclear pores without the need for manual intervention. The proposed pipeline offers an efficient and scalable approach to segmenting and quantifying nuclear pores in 3D microscopy images. Despite the ongoing challenges of computational intensity and data volume, the techniques developed in this study provide insights into the automation of complex biological image analysis tasks, with potential applications extending beyond the detection of nuclear pores.
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    Sensemaking in Immersive Space to Think: Exploring Evolution, Expertise, Familiarity, and Organizational Strategies
    Davidson, Kylie Marie (Virginia Tech, 2024-08-20)
    Sensemaking is the way in which we understand the world around us. Pirolli and Card developed a sensemaking model related to intelligence analysis, which involves taking raw, unstructured data, analyzing it, and presenting a report of the findings. With lower-cost immersive technologies becoming more popular, new opportunities exist to leverage embodied and distributed cognition to better support sensemaking by providing vast, immersive space for creating meaningful schemas (organizational structures) during an analysis task. This work builds on prior work in immersive analytics on the concept of Immersive Space to Think (IST), which provides analysts with immersive space to physically navigate and use to organize information during a sensemaking task. In this work, we performed several studies that aimed to understand how IST supports sensemaking and how we can develop additional features to better aid analysts while they complete sensemaking in immersive analytics systems, focusing on non-quantitative data analysis. In a series of exploratory user studies, we aimed to understand how users' sensemaking process evolves during multiple session analyses, which identified how the participants refined their use of the immersive space into later stages of the sensemaking process. Another exploratory user study highlighted how professional analysts and novice users share many similarities in immersive analytic tool usage during sensemaking within IST. In addition to looking at multi-session analysis tasks, we also explored how sensemaking strategies change as users become more familiar with the immersive analytics tool usage in an exploratory study that utilized multiple analysis tasks completed over a series of three user study sessions. Lastly, we conducted a comparative user study to evaluate how the addition of new organizational features, clustering, and linking affect sensemaking within IST. Overall, our studies expanded the IST tool set and gathered an enhanced understanding of how immersive space is utilized during analysis tasks within IST.
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    Visual Analytics for High Dimensional Simulation Ensembles
    Dahshan, Mai Mansour Soliman Ismail (Virginia Tech, 2021-06-10)
    Recent advancements in data acquisition, storage, and computing power have enabled scientists from various scientific and engineering domains to simulate more complex and longer phenomena. Scientists are usually interested in understanding the behavior of a phenomenon in different conditions. To do so, they run multiple simulations with different configurations (i.e., parameter settings, boundary/initial conditions, or computational models), resulting in an ensemble dataset. An ensemble empowers scientists to quantify the uncertainty in the simulated phenomenon in terms of the variability between ensemble members, the parameter sensitivity and optimization, and the characteristics and outliers within the ensemble members, which could lead to valuable insight(s) about the simulated model. The size, complexity, and high dimensionality (e.g., simulation input and output parameters) of simulation ensembles pose a great challenge in their analysis and exploration. Ensemble visualization provides a convenient way to convey the main characteristics of the ensemble for enhanced understanding of the simulated model. The majority of the current ensemble visualization techniques are mainly focused on analyzing either the ensemble space or the parameter space. Most of the parameter space visualizations are not designed for high-dimensional data sets or did not show the intrinsic structures in the ensemble. Conversely, ensemble space has been visualized either as a comparative visualization of a limited number of ensemble members or as an aggregation of multiple ensemble members omitting potential details of the original ensemble. Thus, to unfold the full potential of simulation ensembles, we designed and developed an approach to the visual analysis of high-dimensional simulation ensembles that merges sensemaking, human expertise, and intuition with machine learning and statistics. In this work, we explore how semantic interaction and sensemaking could be used for building interactive and intelligent visual analysis tools for simulation ensembles. Specifically, we focus on the complex processes that derive meaningful insights from exploring and iteratively refining the analysis of high dimensional simulation ensembles when prior knowledge about ensemble features and correlations is limited or/and unavailable. We first developed GLEE (Graphically-Linked Ensemble Explorer), an exploratory visualization tool that enables scientists to analyze and explore correlations and relationships between non-spatial ensembles and their parameters. Then, we developed Spatial GLEE, an extension to GLEE that explores spatial data while simultaneously considering spatial characteristics (i.e., autocorrelation and spatial variability) and dimensionality of the ensemble. Finally, we developed Image-based GLEE to explore exascale simulation ensembles produced from in-situ visualization. We collaborated with domain experts to evaluate the effectiveness of GLEE using real-world case studies and experiments from different domains. The core contribution of this work is a visual approach that enables the exploration of parameter and ensemble spaces for 2D/3D high dimensional ensembles simultaneously, three interactive visualization tools that explore search, filter, and make sense of non-spatial, spatial, and image-based ensembles, and usage of real-world cases from different domains to demonstrate the effectiveness of the proposed approach. The aim of the proposed approach is to help scientists gain insights by answering questions or testing hypotheses about the different aspects of the simulated phenomenon or/and facilitate knowledge discovery of complex datasets.