Automatic contouring based on the gray value image

The automatic contouring algorithm in the ORMIR_XCT package generates the periosteal mask of the distal and proximal bones of the wrist joint as well as a joint masking combining the distal and proximal bone masks. It uses the gray value image as input.

Function¶

To use automatic contouring with a gray value image as input, use the following command:

autocontour(img, 
    mu_water, 
    rescale_slope, 
    rescale_intercept)

Input parameter settings¶

HR-pQCT

PCD-CT

µCT

img: SITK image or path to image
mu_water: Linear attenuation coefficient of water. Default = 0.2409
rescale_slope: Slope used to rescale to BMD. Default = 1603.51904
rescale_intercept Intercept used to rescale to BMD. Default = -391.209015

Further information about the function and its inputs can be found [here].

Examples and workflows¶

HR-pQCT

PCD-CT

µCT

Examples of how to use the function for the automatic contouring based on a gray value image as input can be found in:

Workflows that include automatic contouring:

Automatic contouring workflow using the image as input

Citation¶

HR-pQCT

PCD-CT

µCT

If you use this function, please cite it like this:

We used the method based on Buie et al. (2007) and implemented in ORMIR-XCT (Kuczynski et al. (2024)).

References¶

Buie, H. R., Campbell, G. M., Klinck, R. J., MacNeil, J. A., & Boyd, S. K. (2007). Automatic segmentation of cortical and trabecular compartments based on a dual threshold technique for in vivo micro-CT bone analysis. Bone, 41(4), 505–515. 10.1016/j.bone.2007.07.007
Kuczynski, M. T., Neeteson, N. J., Stok, K. S., Burghardt, A. J., Hernandez, M. A. E., Vicory, J., Tse, J. J., Durongbhan, P., Bonaretti, S., Wong, A. K. O., Boyd, S. K., & Manske, S. L. (2024). ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing. Journal of Open Source Software, 9(97), 6084. 10.21105/joss.06084