Maureen van Eijnatten1, Roelof van Dijk2, Johannes Dobbe3, Geert Streekstra3, Juha Koivisto2, Jan Wolff2. 1. Department of Oral and Maxillofacial Surgery, 3D Innovation Lab, VU University Medical Center, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands. Electronic address: m.vaneijnatten@vumc.nl. 2. Department of Oral and Maxillofacial Surgery, 3D Innovation Lab, VU University Medical Center, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands. 3. Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam, The Netherlands.
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.
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.
Authors: Maximilian Jörgens; Alexander M Keppler; Philipp Ahrens; Wolf Christian Prall; Marcel Bergstraesser; Andreas T Bachmeier; Christian Zeckey; Adrian Cavalcanti Kußmaul; Wolfgang Böcker; Julian Fürmetz Journal: Eur J Trauma Emerg Surg Date: 2022-07-26 Impact factor: 2.374
Authors: Maria E S Takahashi; Camila Mosci; Edna M Souza; Sérgio Q Brunetto; Elba Etchebehere; Allan O Santos; Mariana R Camacho; Eliana Miranda; Mariana C L Lima; Barbara J Amorim; Carmino de Souza; Fernando V Pericole; Irene Lorand-Metze; Celso D Ramos Journal: Sci Rep Date: 2019-11-11 Impact factor: 4.379