Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"Cell segmentation"”.
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Probabilistic Multi-Compartment Deformable Model, Application to Cell Segmentation
… The fundamental step of this task is the segmentation of images into regions, given some homogeneity criteria, prior appearance and/or shape information criteria. Specifically, segmentation of cells in microscopic images is the first step in analyzing many biomedical applications. This …
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Pipeline for semi-automatic segmentation of confluent endothelial cell membranes
Changes in cell morphology are important indicators of underlying biological changes. As endothelial cells (EC) heterogeneously respond to stimuli, we seek to quantify EC morphologic heterogeneity and relate it to transcriptome phenotypes; however, existing semi-automatic methods for quantifying …
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Animal Internal Motion Analysis with Unsupervised Machine Learning Methods
… internal motions within biological systems—from cellular migrations during development to repeated contractions in muscle tissue—is essential for comprehending the fundamental mechanisms that drive life processes. This report presents innovative unsupervised machine learning methods to explore …
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Automated Identification and Tracking of Motile Oligodendrocyte Precursor Cells (OPCs) from Time-lapse 3D Microscopic Imaging Data of Cell Clusters in vivo
… into the migration of Oligodendrocyte precursor cells (OPCs) and its role in the central nervous system. However, current practice of image-based OPC motility analysis heavily relies on manual labeling and tracking on 2D max projection of the 3D data, which suffers from massive human labor, …
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Deciphering and modelling the action of immune cells using highly multiplexed imaging and deep learning techniques
Cells of the immune system are capable of responding to foreign antigen, promoting host defense while limiting damage to host tissues, through an act known as selftolerance. T cells, their activation and their effector roles are of particular interest due to their prominent roles in antigen …
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Establishing Image-Based Profiling Methods for Single-Cell Transcriptomics and Morphometrics in the Cephalochordate Amphioxus
… below the diffraction limit of light – at sub-cellular resolution – and in three dimensions, generating such a large volume of imaging data that manual analysis becomes unfeasible. Recent developments in the field of machine learning facilitate high-throughput methodologies for image …
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Mathematical Imaging Tools in Cancer Research - From Mitosis Analysis to Sparse Regularisation
… sound imaging models in four ways: (i) automated cell segmentation and tracking. In cancer drug development, time-lapse light microscopy experiments are conducted for performance validation. The aim is to monitor behaviour of cells in cultures that have previously been treated with chemotherapy …
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Multi-omics characterisation of mouse gastrulation and organogenesis at single-cell resolution
… - are specified. Recent advances in single-cell sequencing technologies have allowed the characterisation of the transcriptional and epigenetic changes during mouse gastrulation and early organogenesis. However, the precise molecular mechanisms that control cell fate decisions are still …
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Understanding cell decision-making using near-infrared quantum dots: One cell, one molecule at a time
Cell receives input signals such growth factors from the environment and decides an appropriate output such as migration, division, differentiation, and apoptosis. This decision making process is tightly controlled, and when misregulated leads to pathogenesis such as cancer, immune disease, and …
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Deep Learning Approach for Cell Nuclear Pore Detection and Quantification over High Resolution 3D Data
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 …
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Unraveling Complexity: Panoptic Segmentation in Cellular and Space Imagery
… the application of semi-supervised learning in segmentation tasks. We focus on panoptic segmentation, a task that combines semantic segmentation (assigning a class to each pixel) and instance segmentation (grouping pixels into different object instances). We choose two segmentation tasks in …