# Copyright (c) 2024-2026 Li Chao, Dong Mengshi and HABIT Contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""Whole-habitat radiomics features (PyRadiomics of the habitat map itself)."""
from __future__ import annotations
from typing import Any, Dict, Optional, Union
from habit.contracts.habitat import HabitatMap
from habit.contracts.subject import Subject
from habit.contracts.table import FeatureTable
from habit.habitat_features._base import single_subject_table
from habit.radiomics._domain import (
DEFAULT_USE_TORCH_RADIOMICS,
binarized_habitat_mask,
build_pyradiomics_extractor,
execute_radiomics,
sitk_image_from_contract,
)
from habit.habitat_features.registry import HabitatFeatureExtractorRegistry
from habit.spec.specs import Spec
__all__ = ["WholeHabitatRadiomicsFeatures"]
[docs]
@HabitatFeatureExtractorRegistry.register("whole_habitat")
class WholeHabitatRadiomicsFeatures:
"""
PyRadiomics features of the habitat map treated as the image itself.
The multi-label habitat map plays BOTH roles: it is the intensity image
(habitat ids as grey values) and, binarised, the ROI mask. This is the
v1 form of the v0.1 ``whole_habitat`` feature type, replicating
``HabitatRadiomicsExtractor.extract_radiomics_features_for_whole_habitat``;
it is the family that quantifies the SHAPE of the habitat partition
(sphericity, surface area, ...) rather than the underlying intensity.
Column names are the bare PyRadiomics feature names with ``diagnostic``
entries dropped, matching the v0.1 ``whole_habitat_radiomics.csv``.
"""
[docs]
def __init__(
self,
params_file: Optional[str] = None,
params: Optional[Dict[str, Any]] = None,
use_torch_radiomics: Union[str, bool] = DEFAULT_USE_TORCH_RADIOMICS,
torch_device: str = "auto",
torch_dtype: str = "float64",
) -> None:
self.params_file = params_file
self.params = dict(params) if params is not None else None
self.use_torch_radiomics = use_torch_radiomics
self.torch_device = str(torch_device)
self.torch_dtype = str(torch_dtype)
self._params_file = self.params_file
self._params = self.params
self._use_torch_radiomics = self.use_torch_radiomics
self._torch_device = self.torch_device
self._torch_dtype = self.torch_dtype
@property
def spec(self) -> Spec:
"""Return the algorithm specification."""
return Spec(
name="whole_habitat",
params={
"params_file": self._params_file,
"params": self._params,
"use_torch_radiomics": self._use_torch_radiomics,
"torch_device": self._torch_device,
"torch_dtype": self._torch_dtype,
},
)
[docs]
def __call__(self, subject: Subject, habitat_map: HabitatMap) -> FeatureTable:
"""
Compute the whole-habitat radiomics family for one subject.
Args:
subject: Owning subject (labels suffice; intensities unused).
habitat_map: Habitat labels; used as image and, binarised, mask.
Returns:
One-row table of PyRadiomics features of the habitat map.
"""
owner = f"habitat_feature_extractor.{self.spec.name}"
extractor = build_pyradiomics_extractor(self._params_file, self._params, owner=owner)
habitat_sitk = sitk_image_from_contract(habitat_map.label_array, habitat_map.geometry)
mask_sitk = binarized_habitat_mask(habitat_sitk)
features = execute_radiomics(
extractor,
habitat_sitk,
mask_sitk,
label=1,
use_torch_radiomics=self._use_torch_radiomics,
torch_device=self._torch_device,
torch_dtype=self._torch_dtype,
subject_id=subject.subject_id,
)
return single_subject_table(
subject_id=subject.subject_id,
features=features,
habitat_map=habitat_map,
spec=self.spec,
)