habit.precision: repeatability screen
Precision analysis: which voxel features deserve to define habitats.
Voxel-wise radiomics is noisy; clustering features that do not survive a simulated re-acquisition produces habitats nobody can reproduce. This package implements the precision screen of Prior et al. (Radiol Artif Intell 2024;6(2):e230118) as composable domain components:
ImagePerturbationimplementations (noise, translation, rotation, optional MONAIbspline_deform) and thePerturbationChaincomposing one simulated retest;precision_panel()/aggregate_panels()computing per-subject and cohort-level ICC tables on voxel feature fields;identify_precise_features()applying the LCL screen across experiments and returning the serialisablePreciseFeatureSet;habitat_stability()scoring habitat maps under perturbation;align_habitat_map()remapping independently clustered labels of the same voxels onto a reference (overlap Hungarian);align_habitat_maps_to_prototypes()naming habitats across subjects by iterative matching to shared (or frozen) prototypes.
User guide: Habitat Guide
Precise voxel features ·
Matching habitat labels across subjects.
Recipe wrapper: identify_precise_voxel_features().
Simulated-retest perturbations and the ICC intersection that decides
which extracted voxel columns may define habitats (Prior et al.,
Radiol Artif Intell 2024;6(2):e230118). Label matching lives here too:
align_habitat_map (voxel overlap) for two maps of the same voxels and
align_habitat_maps_to_prototypes for different subjects, including
naming a new cohort with frozen prototypes. Method
choice, algorithm, and literature: Matching habitat labels across fits and subjects.
Classes
Turn one subject into a perturbed copy of itself: a simulated re-acquisition. |
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Add zero-mean Gaussian noise to every image of a subject. |
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Translate image content by a (random) sub-voxel shift. |
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Rotate image content by a small fixed angle about the image centre. |
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Sub-voxel translation and small-angle rotation in ONE resample. |
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MONAI elastic / B-spline free-form warp of every image and ROI. |
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Uniformly grow or shrink every ROI (MIRP |
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Locally grow/shrink ROI boundaries where image gradient is low. |
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Add or remove whole axial slices at the superior/inferior ROI ends. |
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Apply several image perturbations in sequence to one subject. |
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Voxel features that passed the precision screen, with the evidence. |
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Name-to-implementation registry for image perturbations. |
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Cohort habitat maps renamed onto shared prototypes. |
Functions
Return a same-grid perturbed copy of one image. |
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Simulated-retest chain of Prior et al. 2024 / MIRP 1.2.0 Appendix S2. |
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Compute the per-feature ICC panel of ONE subject under ONE experiment. |
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Aggregate per-subject panels into the cohort-level panel. |
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Select the features that clear the LCL threshold in EVERY experiment. |
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Remap |
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Give every subject's habitats one shared set of names (cohort matching). |
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Score habitat stability between a reference map and perturbed maps. |