ClinicaDL dictionary

clinicadl/dictionary/suffixes.py

@@ -0,0 +1,7 @@
+TSV = ".tsv"
+JSON = ".json"
+LOG = ".log"
+PT = ".pt"
+NII = ".nii"
+GZ = ".gz"
+NII_GZ = NII + GZ

clinicadl/dictionary/utils.py

@@ -0,0 +1 @@
+SEP = "\t"

clinicadl/dictionary/words.py

@@ -0,0 +1,32 @@
+AGE = "age"
+AUGMENTATION = "augmentation"
+BASELINE = "baseline"
+BEST = "best"
+CONFIG = "config"
+COUNT = "count"
+CUDA = "cuda"
+DESCRIPTION = "description"
+FOLD = "fold"
+ID = "id"
+IMAGE = "image"
+KFOLD = "k" + FOLD
+LABEL = "label"
+MEAN = "mean"
+OBJECT = "object"
+PARTICIPANT = "participant"
+PARTICIPANT_ID = PARTICIPANT + "_" + ID
+PROPORTION = "proportion"
+SAMPLE = "sample"
+SEX = "sex"
+SESSION = "session"
+SESSION_ID = SESSION + "_" + ID
+SINGLE = "single"
+SPLIT = "split"
+STATISTIC = "statistic"
+STD = "std"
+TEST = "test"
+TMP = "tmp"
+TRAIN = "train"
+TRANSFORMATION = "transformation"
+VALIDATION = "validation"
+VALUE = "value"

clinicadl/transforms/extraction/base.py

@@ -9,6 +9,7 @@
 import torchio as tio
 from pydantic import computed_field
 
+from clinicadl.dictionary.words import IMAGE, LABEL, SAMPLE
 from clinicadl.utils.config import ClinicaDLConfig
 from clinicadl.utils.enum import ExtractionMethod
 
@@ -244,7 +245,7 @@ def extract_tio_sample(
         IndexError
             If 'sample_index' is greater or equal to the number of samples in the image.
         """
-        if not hasattr(tio_image, "image") or not isinstance(
+        if not hasattr(tio_image, IMAGE) or not isinstance(
             tio_image.image, tio.ScalarImage
         ):
             raise AttributeError(
@@ -268,7 +269,7 @@ def extract_tio_sample(
             )
 
         tio_sample.sample = tio_sample.image
-        delattr(tio_sample, "image")
+        delattr(tio_sample, IMAGE)
 
         return tio_sample
 
@@ -278,14 +279,14 @@ def _check_tio_sample(tio_sample: tio.Subject):
         Checks that a TorchIO Subject is a valid sample, i.e. a sample with a TorchIO ScalarImage
         named 'sample', a label named 'label' and a description named 'description'.
         """
-        if not hasattr(tio_sample, "sample") or not isinstance(
+        if not hasattr(tio_sample, SAMPLE) or not isinstance(
             tio_sample.sample, tio.ScalarImage
         ):
             raise AttributeError(
                 "'tio_sample' must contain ScalarImage named 'image'. Got only the following images: "
                 f"{tio_sample.get_images_names()}"
             )
-        if not hasattr(tio_sample, "label"):
+        if not hasattr(tio_sample, LABEL):
             raise AttributeError(
                 "'tio_sample' must contain an attribute named 'label'."
             )

clinicadl/transforms/extraction/image.py

@@ -6,14 +6,13 @@
 import torchio as tio
 from pydantic import PositiveInt, computed_field
 
+from clinicadl.dictionary.suffixes import PT
 from clinicadl.utils.enum import ExtractionMethod
 
 from .base import Extraction, Sample
 
 logger = getLogger("clinicadl.extraction.image")
 
-PT = ".pt"
-
 
 class ImageSample(Sample):
     """

clinicadl/transforms/extraction/patch.py

@@ -6,14 +6,13 @@
 import torchio as tio
 from pydantic import NonNegativeInt, PositiveInt, computed_field, field_validator
 
+from clinicadl.dictionary.suffixes import PT
 from clinicadl.utils.enum import ExtractionMethod
 
 from .base import Extraction, Sample
 
 logger = getLogger("clinicadl.extraction.patch")
 
-PT = ".pt"
-
 
 class PatchSample(Sample):
     """

clinicadl/transforms/extraction/slice.py

@@ -14,6 +14,7 @@
 )
 from typing_extensions import Self
 
+from clinicadl.dictionary.suffixes import PT
 from clinicadl.utils.enum import (
     ExtractionMethod,
     SliceDirection,
@@ -24,8 +25,6 @@
 
 logger = getLogger("clinicadl.extraction.slice")
 
-PT = ".pt"
-
 
 class SliceSample(Sample):
     """

clinicadl/transforms/transforms.py

@@ -4,6 +4,7 @@
 import torchvision.transforms as torch_transforms
 from pydantic import model_validator
 
+from clinicadl.dictionary.words import AUGMENTATION, IMAGE, OBJECT, TRANSFORMATION
 from clinicadl.transforms.extraction import Extraction, Image
 from clinicadl.transforms.factory import (
     MinMaxNormalization,
@@ -115,8 +116,8 @@ def __str__(self) -> str:
 
         def _to_str(
             list_: list[Callable] = [],
-            object_: str = "object",
-            transfo_: str = "transformation",
+            object_: str = OBJECT,
+            transfo_: str = TRANSFORMATION,
         ):
             str_ = ""
             if list_:
@@ -128,13 +129,13 @@ def _to_str(
 
             return str_
 
-        transform_str += _to_str(self.image_transforms, object_="image")
-        transform_str += _to_str(self.object_transforms, object_="object")
+        transform_str += _to_str(self.image_transforms, object_=IMAGE)
+        transform_str += _to_str(self.object_transforms, object_=OBJECT)
         transform_str += _to_str(
-            self.image_augmentation, object_="image", transfo_="augmentation"
+            self.image_augmentation, object_=IMAGE, transfo_=AUGMENTATION
         )
         transform_str += _to_str(
-            self.object_augmentation, object_="object", transfo_="augmentation"
+            self.object_augmentation, object_=OBJECT, transfo_=AUGMENTATION
         )
 
         return transform_str

clinicadl/transforms/utils.py

@@ -3,6 +3,8 @@
 import torch
 import torchio as tio
 
+from clinicadl.dictionary.words import LABEL
+
 
 def get_tio_image(
     image: torch.Tensor,
@@ -32,9 +34,9 @@ def get_tio_image(
     tio_image = tio.Subject(image=tio.ScalarImage(tensor=image))
 
     if isinstance(label, torch.Tensor):
-        tio_image.add_image(tio.LabelMap(tensor=label), "label")
+        tio_image.add_image(tio.LabelMap(tensor=label), LABEL)
     else:
-        setattr(tio_image, "label", label)
+        setattr(tio_image, LABEL, label)
 
     for name, mask in masks.items():
         tio_image.add_image(tio.LabelMap(tensor=mask), name)