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The curve integration method is comparable to manual segmentation for the analysis of bone/scaffold composites using micro‐CT
Author(s) -
Hilldore Amanda J.,
Morgan Abby W.,
Woodard Joseph R.,
Wagoner Johnson Amy J.
Publication year - 2009
Publication title -
journal of biomedical materials research part b: applied biomaterials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 108
eISSN - 1552-4981
pISSN - 1552-4973
DOI - 10.1002/jbm.b.31178
Subject(s) - segmentation , scaffold , biomedical engineering , computer science , x ray microtomography , materials science , artificial intelligence , pattern recognition (psychology) , medicine , radiology
Abstract Microcomputed tomography (micro‐CT) is becoming a more common imaging technique in tissue engineering and has been used to characterize scaffold pore size, pore fraction, and bone ingrowth, among other characteristics. Despite the increasingly widespread use, no standards exist for segmenting images. Manual segmentation, a common segmentation method, is subjective, time consuming, and has been shown to be inaccurate and unreliable. The curve integration method was previously introduced as a method to accurately calculate the volume fraction of constituents in bone scaffolds from micro‐CT data. In this article, the curve integration method is compared to manual image segmentation in order to validate the former method. Three cases are presented from two in vivo bone regeneration studies that include cross‐sections from a rabbit calvarial defect used to study drug delivery, and cross‐sections and small volumes of hydroxyapatite scaffold‐bone composites from a porcine intramuscular study. The analysis shows that the curve integration method models the data accurately and can be used to calculate volume fractions of the materials in the sample. Furthermore, the curve integration method is faster and less labor intensive than manual image segmentation. © 2008 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 2009