[ENH] pet-volume class ApplySegmentationDeformation should inherit from Nipype ApplyDeformations

clinica/pipelines/pet/volume/pipeline.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from nipype import config
 
 from clinica.pipelines.engine import GroupPipeline
@@ -34,7 +32,7 @@ def _check_custom_dependencies(self) -> None:
         """Check dependencies that can not be listed in the `info.json` file."""
         pass
 
-    def get_input_fields(self) -> List[str]:
+    def get_input_fields(self) -> list[str]:
         """Specify the list of possible inputs of this pipeline.
 
         Returns
@@ -53,7 +51,7 @@ def get_input_fields(self) -> List[str]:
             "reference_mask",
         ]
 
-    def get_output_fields(self) -> List[str]:
+    def get_output_fields(self) -> list[str]:
         """Specify the list of possible outputs of this pipeline.
 
         Returns

clinica/pipelines/pet/volume/utils.py

@@ -1,6 +1,6 @@
 from os import PathLike
 from pathlib import Path
-from typing import List, Tuple, Union
+from typing import Union
 
 import nibabel as nib
 import numpy as np
@@ -35,7 +35,7 @@ def init_input_node(pet_nii: str) -> str:
     return pet_nii
 
 
-def _check_non_empty_tissue_list(tissues: List[PathLike]) -> None:
+def _check_non_empty_tissue_list(tissues: list[PathLike]) -> None:
     """Check that provided list is non-empty."""
     if len(tissues) == 0:
         raise RuntimeError(
@@ -44,8 +44,8 @@ def _check_non_empty_tissue_list(tissues: List[PathLike]) -> None:
 
 
 def _aggregate_tissue_images(
-    tissues: List[PathLike],
-) -> Tuple[np.ndarray, np.ndarray, Nifti1Header]:
+    tissues: list[PathLike],
+) -> tuple[np.ndarray, np.ndarray, Nifti1Header]:
     """Aggregates the image data contained in the tissue images provided.
 
     Parameters
@@ -74,7 +74,7 @@ def _aggregate_tissue_images(
     return data, first_image.affine, first_image.header
 
 
-def create_binary_mask(tissues: List[PathLike], threshold: float = 0.3) -> Path:
+def create_binary_mask(tissues: list[PathLike], threshold: float = 0.3) -> Path:
     """Create a binary mask Nifti1Image from the list of tissues.
 
     Tissue images are summed and the result is thresholded with the
@@ -135,7 +135,7 @@ def apply_binary_mask(image: Path, binary_mask: Path) -> Path:
     return masked_image_path
 
 
-def create_pvc_mask(tissues: List[PathLike]) -> Path:
+def create_pvc_mask(tissues: list[PathLike]) -> Path:
     """Create a pvc mask from tissue list.
 
     Parameters
@@ -224,7 +224,7 @@ def normalize_to_reference(pet_image: Path, region_mask: Path) -> Path:
     return suvr_pet_path
 
 
-def compute_atlas_statistics(image: Path, atlas_names: List[str]) -> List[Path]:
+def compute_atlas_statistics(image: Path, atlas_names: list[str]) -> list[Path]:
     """Generate regional measure from atlas_list in TSV files.
 
     For each atlas name provided it calculates for the input image the mean

clinica/pipelines/t1_volume_tissue_segmentation/t1_volume_tissue_segmentation_utils.py

@@ -7,11 +7,10 @@
 from nipype.interfaces.base import (
     File,
     InputMultiPath,
-    OutputMultiPath,
-    TraitedSpec,
     traits,
 )
-from nipype.interfaces.spm.base import SPMCommand, SPMCommandInputSpec
+from nipype.interfaces.spm.base import SPMCommandInputSpec
+from nipype.interfaces.spm.preprocess import ApplyDeformations
 from nipype.utils.filemanip import filename_to_list, list_to_filename
 
 
@@ -129,11 +128,7 @@ class ApplySegmentationDeformationInput(SPMCommandInputSpec):
     )
 
 
-class ApplySegmentationDeformationOutput(TraitedSpec):
-    out_files = OutputMultiPath(File(exists=True), desc="Transformed files")
-
-
-class ApplySegmentationDeformation(SPMCommand):
+class ApplySegmentationDeformation(ApplyDeformations):
     """Uses SPM to apply a deformation field obtained from Segmentation routine to a given file
 
     Examples
@@ -147,10 +142,6 @@ class ApplySegmentationDeformation(SPMCommand):
     """
 
     input_spec = ApplySegmentationDeformationInput
-    output_spec = ApplySegmentationDeformationOutput
-
-    _jobtype = "util"
-    _jobname = "defs"
 
     def _format_arg(self, opt, spec, val):
         """Convert input to appropriate format for spm"""
@@ -159,11 +150,3 @@ def _format_arg(self, opt, spec, val):
         if opt == "in_files":
             return np.array(filename_to_list(val), dtype=object)
         return val
-
-    def _list_outputs(self):
-        outputs = self._outputs().get()
-        outputs["out_files"] = []
-        for filename in self.inputs.in_files:
-            _, fname = os.path.split(filename)
-            outputs["out_files"].append(os.path.realpath("w%s" % fname))
-        return outputs