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3D reconstruction of FIB tomography data using machine learning
Citation Link: https://doi.org/10.15480/882.13216
Publikationstyp
Doctoral Thesis
Date Issued
2024
Sprache
English
Author(s)
Sardhara, Trushal
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2024-08-06
Institute
TORE-DOI
Citation
Technische Universität Hamburg (2024)
This thesis introduces methods for accurate 3D reconstruction of FIB tomography data using machine learning. It addresses challenges in obtaining large datasets and ground truth values, proposing a method for virtual FIB tomography data generation and an isotropy-based validation method. ML-based segmentation methods tackle issues in BSE images, like the shine-through effect and image intensity ambiguities. Integrating the slice repositioning method with image inpainting resolves inconsistencies in slice thicknesses in FIB tomography data. Additionally, a multimodal machine learning approach using multivoltage images further enhances 3D reconstruction accuracy.
Subjects
Machine Learning
3D Reconstruction
FIB tomography
Synthetic Data
Domain Adaptation
Generative ML
DDC Class
006.3: Artificial Intelligence
621.3: Electrical Engineering, Electronic Engineering
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Sardhara_Trushal_3DReconstruction_ML_2024.pdf
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