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A Micro-computed Tomography Database and Reference Implementation for Parallel Computations of Trabecular Thickness and Spacing Cover

A Micro-computed Tomography Database and Reference Implementation for Parallel Computations of Trabecular Thickness and Spacing

Open Access
|Mar 2022

Abstract

We provide a well-defined accurate brute force implementation without approximations as a reference to support future development of correct and fast algorithms to measure the trabecular thickness and spacing. Using artificial ellipsoid examples, the systematic error is shown to be related to the voxelization, which can be reduced by upsampling. Furthermore, we collected a database of 40 three-dimensional micro-computed tomography images of trabecular bone regions of a sheep femur. With the coverage of a broad range of trabecular thickness and spacing, the database is suitable for assessing the correctness of algorithms for the measurements of trabecular thickness and spacing. The bone data is shared publicly in the online repository ‘Figshare’ and the brute force code is freely available in Code Ocean. Scientists are encouraged to implement faster algorithms, e.g., graphic processing unit-accelerated, with accurate or controlled approximate behaviour to compute trabecular thickness and spacing. Especially, the provided data can be re-employed together with the reference algorithm to evaluate their accuracies.

DOI: https://doi.org/10.5334/jors.360 | Journal eISSN: 2049-9647
Language: English
Submitted on: Dec 4, 2020
Accepted on: Feb 20, 2022
Published on: Mar 16, 2022
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2022 Thi-Ngoc-Thu Nguyen, Andreas Höfter, Kevin Leonardic, Stefanie Rosenhain, Fabian Kiessling, Wannida Sae-Tang, Uwe Naumann, Felix Gremse, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.