title: Improving reliability of pQCT-derived muscle area and density measures using a watershed algorithm for muscle and fat segmentation.
abstract: In peripheral quantitative computed tomography scans of the calf muscles, segmentation of muscles from subcutaneous fat is challenged by muscle fat infiltration. Threshold-based edge detection segmentation by manufacturer software fails when muscle boundaries are not smooth. This study compared the test-retest precision error for muscle-fat segmentation using the threshold-based edge detection method vs manual segmentation guided by the watershed algorithm. Three clinical populations were investigated: younger adults, older adults, and adults with spinal cord injury (SCI). The watershed segmentation method yielded lower precision error (1.18%-2.01%) and higher (p<0.001) muscle density values (70.2±9.2 mg/cm3) compared with threshold-based edge detection segmentation (1.77%-4.06% error, 67.4±10.3 mg/cm3). This was particularly true for adults with SCI (precision error improved by 1.56% and 2.64% for muscle area and density, respectively). However, both methods still provided acceptable precision with error well under 5%. Bland-Altman analyses showed that the major discrepancies between the segmentation methods were found mostly among participants with SCI where more muscle fat infiltration was present. When examining a population where fatty infiltration into muscle is expected, the watershed algorithm is recommended for muscle density and area measurement to enable the detection of smaller change effect sizes.
authors.affiliation: Department of Medicine, McMaster University, Hamilton, Ontario, Canada. Electronic address: wongko@mcmaster.ca.
authors.forename: Andy Kin On
authors.initials: AK
authors.lastname: Wong
authors.affiliation: Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada.
authors.forename: Kayla
authors.initials: K
authors.lastname: Hummel
authors.affiliation: Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada.
authors.forename: Cameron
authors.initials: C
authors.lastname: Moore
authors.affiliation: Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Karen A
authors.initials: KA
authors.lastname: Beattie
authors.affiliation: Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Sami
authors.initials: S
authors.lastname: Shaker
authors.affiliation: Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
authors.forename: B Catharine
authors.initials: BC
authors.lastname: Craven
authors.affiliation: Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Adachi
authors.affiliation: Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Papaioannou
authors.affiliation: Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
authors.forename: Lora
authors.initials: L
authors.lastname: Giangregorio
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods / Q000592:standards; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging*; D058996:Quality Improvement; D015203:Reproducibility of Results; D012984:Software; D050151:Subcutaneous Fat / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods
keywords: Muscle cross-sectional area; muscle density; pQCT; precision; watershed algorithm