title: The impact of manual threshold selection in medical additive manufacturing.
abstract: OBJECTIVE
  Medical additive manufacturing requires standard tessellation language (STL) models. Such models are commonly derived from computed tomography (CT) images using thresholding. Threshold selection can be performed manually or automatically. The aim of this study was to assess the impact of manual and default threshold selection on the reliability and accuracy of skull STL models using different CT technologies.
  
  
  METHODS
  One female and one male human cadaver head were imaged using multi-detector row CT, dual-energy CT, and two cone-beam CT scanners. Four medical engineers manually thresholded the bony structures on all CT images. The lowest and highest selected mean threshold values and the default threshold value were used to generate skull STL models. Geometric variations between all manually thresholded STL models were calculated. Furthermore, in order to calculate the accuracy of the manually and default thresholded STL models, all STL models were superimposed on an optical scan of the dry female and male skulls ("gold standard").
  
  
  RESULTS
  The intra- and inter-observer variability of the manual threshold selection was good (intra-class correlation coefficients >0.9). All engineers selected grey values closer to soft tissue to compensate for bone voids. Geometric variations between the manually thresholded STL models were 0.13 mm (multi-detector row CT), 0.59 mm (dual-energy CT), and 0.55 mm (cone-beam CT). All STL models demonstrated inaccuracies ranging from -0.8 to +1.1 mm (multi-detector row CT), -0.7 to +2.0 mm (dual-energy CT), and -2.3 to +4.8 mm (cone-beam CT).
  
  
  CONCLUSIONS
  This study demonstrates that manual threshold selection results in better STL models than default thresholding. The use of dual-energy CT and cone-beam CT technology in its present form does not deliver reliable or accurate STL models for medical additive manufacturing. New approaches are required that are based on pattern recognition and machine learning algorithms.
authors.affiliation: Department of Oral and Maxillofacial Surgery/Oral Pathology, 3D Innovation Lab, VU University Medical Center, De Boelelaan 1118, 1081 HV, Amsterdam, The Netherlands. m.vaneijnatten@vumc.nl.
authors.forename: Maureen
authors.identifier: http://orcid.org/0000-0001-9208-283X
authors.initials: M
authors.lastname: van Eijnatten
authors.affiliation: Department of Oral and Maxillofacial Surgery/Oral Pathology, 3D Innovation Lab, VU University Medical Center, De Boelelaan 1118, 1081 HV, Amsterdam, The Netherlands.|Research and Technology, Planmeca Ltd, Helsinki, Finland.
authors.forename: Juha
authors.initials: J
authors.lastname: Koivisto
authors.affiliation: Research and Technology, Planmeca Ltd, Helsinki, Finland.
authors.forename: Kalle
authors.initials: K
authors.lastname: Karhu
authors.affiliation: Department of Oral and Maxillofacial Surgery/Oral Pathology, 3D Innovation Lab, VU University Medical Center, De Boelelaan 1118, 1081 HV, Amsterdam, The Netherlands.
authors.forename: Tymour
authors.initials: T
authors.lastname: Forouzanfar
authors.affiliation: Department of Oral and Maxillofacial Surgery/Oral Pathology, 3D Innovation Lab, VU University Medical Center, De Boelelaan 1118, 1081 HV, Amsterdam, The Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: Wolff
mesh_terms: D000465:Algorithms; D005260:Female; D006257:Head / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D015203:Reproducibility of Results; D012886:Skull / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Additive manufacturing; Computed tomography (CT); Medical imaging; Segmentation; Three-dimensional (3D)printing; Thresholding