title: Automated cortical bone segmentation for multirow-detector CT imaging with validation and application to human studies.
abstract: OBJECTIVE
  Cortical bone supports and protects human skeletal functions and plays an important role in determining bone strength and fracture risk. Cortical bone segmentation at a peripheral site using multirow-detector CT (MD-CT) imaging is useful for in vivo assessment of bone strength and fracture risk. Major challenges for the task emerge from limited spatial resolution, low signal-to-noise ratio, presence of cortical pores, and structural complexity over the transition between trabecular and cortical bones. An automated algorithm for cortical bone segmentation at the distal tibia from in vivo MD-CT imaging is presented and its performance and application are examined.
  
  
  METHODS
  The algorithm is completed in two major steps-(1) bone filling, alignment, and region-of-interest computation and (2) segmentation of cortical bone. After the first step, the following sequence of tasks is performed to accomplish cortical bone segmentation-(1) detection of marrow space and possible pores, (2) computation of cortical bone thickness, detection of recession points, and confirmation and filling of true pores, and (3) detection of endosteal boundary and delineation of cortical bone. Effective generalizations of several digital topologic and geometric techniques are introduced and a fully automated algorithm is presented for cortical bone segmentation.
  
  
  RESULTS
  An accuracy of 95.1% in terms of volume of agreement with manual outlining of cortical bone was observed in human MD-CT scans, while an accuracy of 88.5% was achieved when compared with manual outlining on postregistered high resolution micro-CT imaging. An intraclass correlation coefficient of 0.98 was obtained in cadaveric repeat scans. A pilot study was conducted to describe gender differences in cortical bone properties. This study involved 51 female and 46 male participants (age: 19-20 yr) from the Iowa Bone Development Study. Results from this pilot study suggest that, on average after adjustment for height and weight differences, males have thicker cortex (mean difference 0.33 mm and effect size 0.92 at the anterior region) with lower bone mineral density (mean difference -28.73 mg/cm(3) and effect size 1.35 at the posterior region) as compared to females.
  
  
  CONCLUSIONS
  The algorithm presented is suitable for fully automated segmentation of cortical bone in MD-CT imaging of the distal tibia with high accuracy and reproducibility. Analysis of data from a pilot study demonstrated that the cortical bone indices allow quantification of gender differences in cortical bone from MD-CT imaging. Application to larger population groups, including those with compromised bone, is needed.
authors.affiliation: Department of Electrical and Computer Engineering, College of Engineering, Iowa City, Iowa 52242.
authors.forename: Cheng
authors.initials: C
authors.lastname: Li
authors.affiliation: Department of Electrical and Computer Engineering, College of Engineering, Iowa City, Iowa 52242.
authors.forename: Dakai
authors.initials: D
authors.lastname: Jin
authors.affiliation: Department of Electrical and Computer Engineering, College of Engineering, Iowa City, Iowa 52242.
authors.forename: Cheng
authors.initials: C
authors.lastname: Chen
authors.affiliation: Department of Epidemiology, College of Public Health, Iowa City, Iowa 52242.
authors.forename: Elena M
authors.initials: EM
authors.lastname: Letuchy
authors.affiliation: Department of Health and Human Physiology, College of Liberal Arts and Sciences, Iowa City, Iowa 52242.
authors.forename: Kathleen F
authors.initials: KF
authors.lastname: Janz
authors.affiliation: Department of Epidemiology, College of Public Health, Iowa City, Iowa 52242.
authors.forename: Trudy L
authors.initials: TL
authors.lastname: Burns
authors.affiliation: Department of Epidemiology, College of Public Health, Iowa City, Iowa 52242.
authors.forename: James C
authors.initials: JC
authors.lastname: Torner
authors.affiliation: Department of Preventive and Community Dentistry, College of Dentistry, Iowa City, Iowa 52242 and Department of Epidemiology, College of Public Health, Iowa City, Iowa 52242.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Levy
authors.affiliation: Department of Electrical and Computer Engineering, College of Engineering, Iowa City, Iowa 52242 and Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242.
authors.forename: Punam K
authors.initials: PK
authors.lastname: Saha
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D010865:Pilot Projects; D015203:Reproducibility of Results; D013977:Tibia / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*; D055815:Young Adult