A deep-learning-based workflow for reconstructing and segmenting challenging sets of time-resolved X-ray micro-computed tomography data-Reference-Cited by-同舟云学术

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A deep-learning-based workflow for reconstructing and segmenting challenging sets of time-resolved X-ray micro-computed tomography data

Published:2024-09 Issue: Volume:27 Page:101796
ISSN:2352-7110
Container-title:SoftwareX
language:en
Short-container-title:SoftwareX

Author:

Waldner Samuel,Huwyler Jörg,Puchkov Maxim^ORCID

Funder

Universität Basel

Publisher

Elsevier BV

Reference18 articles.

1. High resolution X-ray detector for synchrotron-based microtomography;Stampanoni;Nucl Instrum Methods Phys Res Sect Accel Spectromet Detect Assoc Equip,2002

2. Fast in situ 3D nanoimaging: a new tool for dynamic characterization in materials science;Villanova;Mater Today,2017

3. Using X-ray tomoscopy to explore the dynamics of foaming metal;García-Moreno;Nat Commun,2019

4. Advanced analysis of disintegrating pharmaceutical compacts using deep learning-based segmentation of time-resolved micro-tomography images;Waldner;Heliyon,2024

5. A modular U-net for automated segmentation of X-ray tomography images in composite materials;Bertoldo;Front Mater,2021

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