Re-segmentation guidelines#

Re-segmentation selects which voxels inside a region of interest (ROI) contribute to intensity and texture analysis. Apply it after resampling and before discretization. It can exclude voxels outside a selected intensity range or remove intensity outliers.

Which mask changes?#

Z-Rad follows the Image Biomarker Standardisation Initiative (IBSI) distinction between two masks:

Mask

Purpose

Effect of re-segmentation

Morphological mask

Defines the ROI shape used for morphology.

Unchanged.

Intensity mask

Selects voxels for intensity and texture analysis.

Voxels can be removed, leaving holes or disconnected regions.

Intensity statistics, intensity histogram, intensity-volume histogram (IVH), and most texture features use the intensity mask. Grey level distance zone matrix (GLDZM) features use both masks: the morphological mask defines distances to the ROI boundary. See Feature extraction concepts for the feature families.

Range re-segmentation#

Range re-segmentation keeps voxels whose image intensities fall within the selected lower and upper bounds, including both endpoints. For example, a CT protocol might use [-50, 150] Hounsfield units (HU), while a PET protocol might use a lower standardized uptake value (SUV) threshold such as [3, infinity). These are examples, not default settings for every study.

Use a range that is meaningful for the modality and analysis:

  • CT and PET have calibrated units, so choose and report a range appropriate to the tissue and study protocol.

  • Raw MRI intensities depend on acquisition and scanner settings. Use a common range only when the intensity scale has been standardized and the range can be justified.

In Z-Rad, range selection uses the original image supplied for extraction, even when a filtered image supplies the intensities for feature calculation. This is one reason filtered-image extraction also requires the original image.

Outlier removal#

Outlier removal uses the mean and standard deviation of the valid intensity values inside the ROI. For example, a setting of 3 keeps values within mean - 3 * standard deviation and mean + 3 * standard deviation. The accepted interval therefore depends on each ROI’s intensity distribution. For filtered-image extraction, these statistics use the filtered intensities.

When both methods are enabled, Z-Rad applies the range first and calculates outlier statistics from the remaining voxels:

  1. Keep voxels whose original image intensities fall within the selected range, for example [-50, 150] HU.

  2. Calculate the mean and standard deviation of the retained intensity-mask values.

  3. Remove values outside the selected standard-deviation interval.

The final intensity mask contains only voxels accepted by both rules. Check that enough voxels remain for extraction; see Feature extraction concepts for mask-size requirements.

Configure re-segmentation#

In the GUI’s Radiomics tab, use Intensity Range and Outlier Removal. In Python, apply Resegmenter after IntensityMaskBuilder and before TextureDiscretizer or IVHIntensityDiscretizer. Re-segmentation clears previously prepared texture and IVH images because the intensity population has changed. See Python image workflows for a complete pipeline.

A configured intensity range also affects bin origins and intensity ranges used during discretization; see Discretization guidelines.

What to report#

Record these settings with the extracted features:

  • whether you used range re-segmentation, outlier removal, both, or neither

  • the intensity bounds and units, including whether the upper bound was finite

  • the standard-deviation multiplier for outlier removal

  • that re-segmentation followed resampling and, when both methods were used, range selection preceded outlier statistics

  • whether feature intensities came from the original or a filtered image

  • that re-segmentation changed the intensity mask while retaining the morphological mask