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Relevant bibliographies by topics / 3D Neuron Reconstruction

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Dissertations / Theses

Academic literature on the topic '3D Neuron Reconstruction'

Author: Grafiati

Published: 20 February 2023

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Dissertations / Theses on the topic "3D Neuron Reconstruction"

1

Mayerich, David Matthew. "Acquisition and reconstruction of brain tissue using knife-edge scanning microscopy." Texas A&M University, 2003. http://hdl.handle.net/1969.1/563.

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A fast method for gathering large-scale data sets through the serial sectioning of brain tissue is described. These data sets are retrieved using knife-edge scanning microscopy, a new technique developed in the Brain Networks Laboratory at Texas A&M University. This technique allows the imaging of tissue as it is cut by an ultramicrotome. In this thesis the development of a knife-edge scanner is discussed as well as the scanning techniques used to retrieve high-resolution data sets. Problems in knife-edge scanning microscopy, such as illumination, knife chatter, and focusing are discussed. Tec

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2

Koo, Stephanie C. "3D reconstruction of synaptic and nuclear corticosteroid receptors distribution density in the amygdala: a feasibility study." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/103629/1/Stephanie_Koo_Thesis.pdf.

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This project tested a new way of looking at different types of stress receptors (corticosteroid receptors) in the brain. The method used fluorescent markers to label the stress receptors in brain tissue, and reconstructed microscope images in 3D by using computer software. The thesis details this new method, and demonstrates how it can be used to compare different types of stress receptors, at different locations in the amygdala.

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3

Maack, Nina. "3D Reconstruction of Neural Circuits from Serial EM Images." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-86627.

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4

Grundberg, Måns, and Viktor Altintas. "Generating 3D Scenes From Single RGB Images in Real-Time Using Neural Networks." Thesis, Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43091.

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The ability to reconstruct 3D scenes of environments is of great interest in a number of fields such as autonomous driving, surveillance, and virtual reality. However, traditional methods often rely on multiple cameras or sensor-based depth measurements to accurately reconstruct 3D scenes. In this thesis we propose an alternative, deep learning-based approach to 3D scene reconstruction for objects of interest, using nothing but single RGB images. We evaluate our approach using the Deep Object Pose Estimation (DOPE) neural network for object detection and pose estimation, and the NVIDIA Deep le

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5

Papadopoulos, Georgios. "Towards a 3D building reconstruction using spatial multisource data and computational intelligence techniques." Thesis, Limoges, 2019. http://www.theses.fr/2019LIMO0084/document.

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La reconstruction de bâtiments à partir de photographies aériennes et d’autres données spatiales urbaines multi-sources est une tâche qui utilise une multitude de méthodes automatisées et semi-automatisées allant des processus ponctuels au traitement classique des images et au balayage laser. Dans cette thèse, un système de relaxation itératif est développé sur la base de l'examen du contexte local de chaque bord en fonction de multiples sources d'entrée spatiales (masques optiques, d'élévation, d'ombre et de feuillage ainsi que d'autres données prétraitées, décrites au chapitre 6). Toutes ces

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6

Westerhoff, Malte. "Efficient visualization and reconstruction of 3D geometric models from neuro-biological confocal microscope scans." [S.l. : s.n.], 2001. http://www.diss.fu-berlin.de/2004/19/index.html.

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7

Liu, Ruixu. "Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton157546836015948.

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8

Bartoli, Simone. "Deploying deep learning for 3D reconstruction from monocular video sequences." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22402/.

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3D reconstruction from monocular video sequences is a field of increasingly interest in the late years. Before the growth of deep learning, the retrieve of depth information from single images was possible only with RGBD sensors or algorithmic approaches. However, the availability of more and more data has allowed the training of monocular depth estimation neural networks, introducing innovative data-driven techniques. Since recovering ground-truth labels for depth estimation is very challenging, most of the research has focused on unsupervised or semi-supervised training approaches. The curre

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9

Hanslovsky, Philipp [Verfasser], and Fred [Akademischer Betreuer] Hamprecht. "Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron Microscopy / Philipp Hanslovsky ; Betreuer: Fred Hamprecht." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://d-nb.info/1200021622/34.

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10

Dai, Peilun. "Towards optical connectomics : feasibility of 3D reconstruction of neural morphology using expansion microscopy and in situ molecular barcoding." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120630.

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Thesis: S.M. in Neuroscience, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 27-29).<br>Reconstruction of the 3D morphology of neurons is an essential step towards the analysis and understanding of the structures and functions of neural circuits. Optical microscopy has been a key technology for mapping brain circuits throughout the history of neuroscience due to its low cost, wide usage in biological sciences and ability to read out information-rich molecular signals

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