Almost all European countries lack contemporary
skeletal collections for the development and validation of forensic
anthropological methods. Furthermore, legal, ethical and
practical considerations hinder the development of skeletal collections.
A virtual skeletal database derived from clinical computed
tomography (CT) scans provides a potential solution.
However, clinical CT scans are typically generated with varying
settings. This study investigates the effects of image segmentation
and varying imaging conditions on the precision of virtual
modelled pelves. An adult human cadaver was scanned using
varying imaging conditions, such as scanner type and standard
patient scanning protocol, slice thickness and exposure level. The
pelvis was segmented from the various CT images resulting in virtually modelled pelves. The precision of the virtual modelling
was determined per polygon mesh point. The fraction of mesh
points resulting in point-to-point distance variations of 2 mm or
less (95% confidence interval (CI)) was reported. Colour mapping
was used to visualise modelling variability. At almost all
(>97%) locations across the pelvis, the point-to-point distance
variation is less than 2mm(CI = 95%). In >91% of the locations,
the point-to-point distance variation was less than 1 mm
(CI = 95%). This indicates that the geometric variability of the
virtual pelvis as a result of segmentation and imaging conditions
rarely exceeds the generally accepted linear error of 2 mm.
Colour mapping shows that areas with large variability are predominantly
joint surfaces. Therefore, results indicate that segmented
bone elements from patient-derived CT scans are a sufficiently
precise source for creating a virtual skeletal database.