voidspace

Spacing-aware voidspace analysis for HR-pQCT segmentations.

voidspace is a standalone logic package. It does not segment bone, register scans, create common regions, or resample images. It expects inputs that are already in the image space to analyze.

The command line interface reads common SimpleITK image formats and Scanco AIM masks. AIM masks are read in native scaling, interpreted as foreground where nonzero, and written back as native binary AIM masks using the input geometry.

Workflows

• Cross-sectional: pass a segmented bone image.
• Masked/common-region: pass a segmented bone image and a mask. If both a periosteal contour and a common region are needed, combine them before calling voidspace.
• Registered: pass an already registered segmentation and optional registered mask.
• Dynamic change: pass already aligned baseline and follow-up voidspace masks.

When no periosteal mask is supplied, the segmentation-only workflow estimates the analysis domain from the closed segmentation by excluding background connected to the image border. Supplying an explicit mask remains preferred when that contour is available.

Algorithm

The algorithm follows the original IPL voidspace concept:

1. Remove tiny disconnected bone components.
2. Close segmented bone to fill normal trabecular spacing.
3. Invert the closed bone region to candidate marrow void.
4. Erode candidate void away from trabecular surfaces.
5. Remove tiny candidate void components.
6. Keep all surviving voids and large voids above a physical volume threshold.

Unlike the IPL scripts, morphology uses a spacing-aware spherical or ellipsoidal footprint. The default XtremeCT II settings are:

• closing radius: 0.738 mm
• boundary erosion radius: 0.366 mm
• minimum large-void volume: 16.5 mm3
• bone and void speckle cleanup: 6 voxels

The original IPL code used 72560 or 72660 voxels as scanner-specific large void thresholds in different script/paper contexts. This package treats 16.5 mm3 as the default source of truth and converts it from the supplied image spacing.

CLI

voidspace run-case \
  --segmentation seg.AIM \
  --mask analysis_domain.AIM \
  --output-dir voidspace-out

voidspace compare \
  --baseline-void baseline_voidspace_large_mask.AIM \
  --followup-void followup_voidspace_large_mask.AIM \
  --mask analysis_domain.AIM \
  --output-dir voidspace-change

--mask is optional for both commands. Use --force to overwrite existing outputs.

Python

from voidspace import VoidspaceParameters, run_case

result = run_case(
    segmentation_path="seg.AIM",
    mask_path="analysis_domain.AIM",
    output_dir="voidspace-out",
    parameters=VoidspaceParameters.xtremectii_defaults(),
)
print(result.metrics.volume_mm3)

from voidspace import compare

result = compare(
    baseline_void_path="baseline_voidspace_large_mask.AIM",
    followup_void_path="followup_voidspace_large_mask.AIM",
    mask_path="analysis_domain.AIM",
    output_dir="voidspace-change",
)
print(result.metrics.net_change_volume_mm3)

Citation

For cross-sectional voidspace analysis, cite:

Whittier DE, Burt LA, Boyd SK. A new approach for quantifying localized bone loss by measuring void spaces. Bone. 2021 Feb;143:115785. doi: 10.1016/j.bone.2020.115785. Epub 2020 Dec 2. PMID: 33278655.

For dynamic voidspace analysis, cite:

Whittier DE, Walle M, Atkins PR, Collins CJ, Zumstein MA, Christen P, Lippuner K, Müller R. Structural alterations during fracture healing lead to void spaces developing in surrounding bone microarchitecture. Journal of Bone and Mineral Research. 2025 Jun;40(6):791-798. doi: 10.1093/jbmr/zjaf046.