Derivation of periosteal and endosteal contours taken from transversal long bone cross‐sections limits the accuracy of calculated biomechanical properties. Although several techniques are available for deriving both contours, the effect of these techniques on accuracy of calculated cross‐sectional properties in non‐adults is unknown. We examine a sample of 86 non‐adult femora from birth to 12 years of age to estimate the effect of error in deriving periosteal and endosteal contours on cross‐sectional properties. Midshaft cross‐sections were taken from microCT scans and contours were derived using manual, fully automatic, spline, and ellipse techniques. Agreement between techniques was assessed against manually traced periosteal and endosteal contours using percent prediction error (%PE), reduced major axis analysis, and limits of agreement. The %PEs were highest in the medullary area and lowest in the total area. Mean %PEs were sufficiently below the 5% level of acceptable error, except for medullary areas, but individual values can greatly exceed this 5% boundary given the high standard deviation of %PE means and wide minimum–maximum range of %PEs. Automatic processing produces greater errors than does combination with manual, spline, and ellipse processing. Although periosteal contour is estimated with stronger agreement compared with endosteal contour, error in deriving periosteal contour has a substantially greater effect on calculated section moduli than does error in deriving endosteal contours. We observed no size effect on the resulting bias. Nevertheless, cross‐sectional properties in a younger age category may be estimated with greater error compared with in an older age category. We conclude that non‐adult midshaft cross‐sectional properties can be derived from microCT scans of femoral diaphyses with mean error of < 5% and that derivation of endosteal contour can be simplified by the ellipse technique because fully automatic derivation of endosteal contour may increase the resulting error, especially in small samples.