CT image segmentation methods for bone used in medical additive manufacturing

Maureen van Eijnatten*, Roelof van Dijk, Johannes Dobbe, Geert Streekstra, Juha Koivisto, Jan Wolff

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Aim of the study: The accuracy of additive manufactured medical constructs is limited by errors introduced during image segmentation. The aim of this study was to review the existing literature on different image segmentation methods used in medical additive manufacturing. Methods: Thirty-two publications that reported on the accuracy of bone segmentation based on computed tomography images were identified using PubMed, ScienceDirect, Scopus, and Google Scholar. The advantages and disadvantages of the different segmentation methods used in these studies were evaluated and reported accuracies were compared. Results: The spread between the reported accuracies was large (0.04 mm - 1.9 mm). Global thresholding was the most commonly used segmentation method with accuracies under 0.6 mm. The disadvantage of this method is the extensive manual post-processing required. Advanced thresholding methods could improve the accuracy to under 0.38 mm. However, such methods are currently not included in commercial software packages. Statistical shape model methods resulted in accuracies from 0.25 mm to 1.9 mm but are only suitable for anatomical structures with moderate anatomical variations. Conclusions: Thresholding remains the most widely used segmentation method in medical additive manufacturing. To improve the accuracy and reduce the costs of patient-specific additive manufactured constructs, more advanced segmentation methods are required.

Original languageEnglish
Pages (from-to)6-16
Number of pages11
JournalMedical Engineering and Physics
DOIs
Publication statusPublished - 2018

Cite this

van Eijnatten, M., van Dijk, R., Dobbe, J., Streekstra, G., Koivisto, J., & Wolff, J. (2018). CT image segmentation methods for bone used in medical additive manufacturing. Medical Engineering and Physics, 6-16. https://doi.org/10.1016/j.medengphy.2017.10.008