Whole / Each Habitat Radiomics

whole_habitat

Output

whole_habitat_radiomics.csv

Definition

PyRadiomics run on the multi-label habitat map itself: the habitat label image is passed as both image and mask to PyRadiomics, so voxel intensities are habitat label IDs (not original MR/PET values). Parameters: params_file_of_habitat (optional; bundled habitat preset → habit/resources/radiomics/parameter_habitat.yaml when omitted).

Feature definitions follow PyRadiomics Feature Reference.

Output columns

Column pattern	Description
`{pyradiomics_feature}`	PyRadiomics feature names (no modality suffix)
(excluded)	Columns whose names contain `diagnostic` are dropped before export

Implementation

habit/core/habitat_analysis/habitat_features/builtin_plugins.py (WholeHabitatPlugin) → habitat_radiomics.py

each_habitat

Output

habitat_{k}_radiomics.csv — one file per habitat index k = 1 … K (K = n_habitats from config or habitats.csv)
habitat_count.csv — binary flags has_habitat_1 … has_habitat_K

Definition

For each habitat index k, PyRadiomics is run on the original preprocessed image with the multi-label habitat map as mask and label=k (voxels outside label k are excluded). Uses params_file_of_non_habitat (roi preset), not params_file_of_habitat. Files are written for every k in 1 … K even when a subject’s map lacks that label (empty ROI → NaN / error handling per subject).

Feature definitions follow PyRadiomics Feature Reference.

Output columns

habitat_{k}_radiomics.csv:

Column pattern	Description
`{pyradiomics_feature}_of_{modality}`	One column per PyRadiomics feature × image modality
(excluded)	Columns whose names contain `diagnostic` are dropped before export

habitat_count.csv:

Column	Description
`has_habitat_{k}`	`1` if subject map contains label k, else `0`

Implementation

habit/core/habitat_analysis/habitat_features/builtin_plugins.py (EachHabitatPlugin) → habitat_radiomics.py