Sammendrag
Background: Segmentation of computed tomography (CT) images provides quantitative data on body tissue
composition, which may greatly impact the development and progression of diseases such as type 2 diabetes
mellitus and cancer. We aimed to evaluate the inter- and intraobserver variation of semiautomated segmentation,
to assess whether multiple observers may interchangeably perform this task.
Methods: Anonymised, unenhanced, single mid-abdominal CT images were acquired from 132 subjects from two
previous studies. Semiautomated segmentation was performed using a proprietary software package. Abdominal
muscle compartment (AMC), inter- and intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT) and
subcutaneous adipose tissue (SAT) were identified according to pre-established attenuation ranges. The
segmentation was performed by four observers: an oncology resident with extensive training and three
radiographers with a 2-week training programme. To assess interobserver variation, segmentation of each CT image
was performed individually by two or more observers. To assess intraobserver variation, three of the observers did
repeated segmentations of the images. The distribution of variation between subjects, observers and random noise
was estimated by a mixed effects model. Inter- and intraobserver correlation was assessed by intraclass correlation
coefficient (ICC).
Results: For all four tissue compartments, the observer variations were far lower than random noise by factors
ranging from 1.6 to 3.6 and those between subjects by factors ranging from 7.3 to 186.1. All interobserver ICC was
≥ 0.938, and all intraobserver ICC was ≥ 0.996.
Conclusions: Body composition segmentation showed a very low level of operator dependability. Multiple
observers may interchangeably perform this task with highly reproducible results.
Keywords: Body composition, Abdominal fat, Skeletal muscle, Tomography (X-ray computed), Observer variation
Vis fullstendig beskrivelse
