A comparison of deep learning segmentation models for synchrotron radiation based tomograms of biodegradable bone implants

A comparison of deep learning segmentation models for synchrotron radiation based tomograms of biodegradable bone implants

Introduction: Synchrotron radiation micro-computed tomography (SRμCT) has been used as a non-invasive technique to examine the microstructure and tissue integration of biodegradable bone implants. To be able to characterize parameters regarding the disintegration and osseointegration of such materia...

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Bibliographic Details
Main Authors: André Lopes Marinho, Bashir Kazimi, Hanna Ćwieka, Romy Marek, Felix Beckmann, Regine Willumeit-Römer, Julian Moosmann, Berit Zeller-Plumhoff
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Physics
Subjects:
synchrotron radiation micro-computed tomography
biodegradable materials
magnesium-based alloys
semantic segmentation
convolutional neural networks
Online Access:https://www.frontiersin.org/articles/10.3389/fphy.2024.1257512/full

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https://www.frontiersin.org/articles/10.3389/fphy.2024.1257512/full

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