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Simplified interface to the Insight Toolkit (ITK) for medical image processing. Segmentation, registration, filtering, resampling, morphological operations. Supports DICOM, NIfTI, NRRD, dozens of formats.
Passed 20 of 22 test cases with the skill, and 14 of 22 without it.
and 6 more in the full report
6 fixed14 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
Instantiate the SimpleITK filter object used to compute intensity statistics for labeled image regions.
| Checked | Without skill | With skill |
|---|---|---|
| The code instantiates sitk.LabelIntensityStatisticsImageFilter(). | Missed | Passed |
The instructions your model reads when the skill loads.
SimpleITK simplifies the Insight Toolkit (ITK) for medical image processing: segmentation, registration, filtering, resampling, and morphological operations. Supports DICOM, NIfTI, NRRD, and 50+ file formats.
uv pip install SimpleITK
import SimpleITK as sitk
import numpy as np
image = sitk.ReadImage("ct_scan.nii.gz")
print(image.GetSize(), image.GetSpacing(), image.GetOrigin())
array = sitk.GetArrayFromImage(image)
print(array.shape) # (z, y, x)
binary = sitk.BinaryThreshold(image, lower=200, upper=500, insideValue=1, outsideValue=0)
cc = sitk.ConnectedComponent(binary)
stats = sitk.LabelIntensityStatisticsImageFilter()
stats.Execute(cc, image)
for label in stats.GetLabels():
print(f"Label {label}: mean={stats.GetMean(label):.1f}")
fixed = sitk.ReadImage("template.nii.gz")
moving = sitk.ReadImage("moving.nii.gz")
test cases passed, vs 14 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.