Save output with the same format as brainreg

.github/workflows/test_and_deploy.yml

@@ -3,7 +3,7 @@ name: tests
 on:
   push:
     branches:
-      - '*'
+      - 'main'
     tags:
       - '*'
   pull_request:

brainglobe_registration/elastix/register.py

@@ -1,36 +1,22 @@
-from typing import List, Tuple
+from pathlib import Path
+from typing import List, Optional, Tuple
 
 import itk
 import numpy as np
 import numpy.typing as npt
-from brainglobe_atlasapi import BrainGlobeAtlas
 
-
-def get_atlas_by_name(atlas_name: str) -> BrainGlobeAtlas:
-    """
-    Get a BrainGlobeAtlas object by its name.
-
-    Parameters
-    ----------
-    atlas_name : str
-        The name of the atlas.
-
-    Returns
-    -------
-    BrainGlobeAtlas
-        The BrainGlobeAtlas object.
-    """
-    atlas = BrainGlobeAtlas(atlas_name)
-
-    return atlas
+from brainglobe_registration.utils.utils import (
+    convert_atlas_labels,
+    restore_atlas_labels,
+)
 
 
 def run_registration(
-    atlas_image,
-    moving_image,
-    annotation_image,
+    atlas_image: npt.NDArray,
+    moving_image: npt.NDArray,
     parameter_lists: List[Tuple[str, dict]],
-) -> Tuple[npt.NDArray, itk.ParameterObject, npt.NDArray]:
+    output_directory: Optional[Path] = None,
+) -> Tuple[npt.NDArray, itk.ParameterObject]:
     """
     Run the registration process on the given images.
 
@@ -40,19 +26,17 @@
         The atlas image.
     moving_image : npt.NDArray
         The moving image.
-    annotation_image : npt.NDArray
-        The annotation image.
-    parameter_lists : List[tuple[str, dict]], optional
-        The list of parameter lists, by default None
+    parameter_lists : List[tuple[str, dict]]
+        The list of registration parameters, one for each transform.
+    output_directory : Optional[Path], optional
+        The output directory for the registration results, by default None
 
     Returns
     -------
     npt.NDArray
         The result image.
     itk.ParameterObject
         The result transform parameters.
-    npt.NDArray
-        The transformed annotation image.
     """
     # convert to ITK, view only
     atlas_image = itk.GetImageViewFromArray(atlas_image).astype(itk.F)
@@ -66,33 +50,200 @@
     parameter_object = setup_parameter_object(parameter_lists=parameter_lists)
 
     elastix_object.SetParameterObject(parameter_object)
-
-    # update filter object
     elastix_object.UpdateLargestPossibleRegion()
 
     # get results
     result_image = elastix_object.GetOutput()
     result_transform_parameters = elastix_object.GetTransformParameterObject()
-    temp_interp_order = result_transform_parameters.GetParameter(
+
+    if output_directory:
+        file_names = [
+            f"{output_directory}/TransformParameters.{i}.txt"
+            for i in range(len(parameter_lists))
+        ]
+
+        itk.ParameterObject.WriteParameterFile(
+            result_transform_parameters, file_names
+        )
+
+    return (
+        np.asarray(result_image),
+        result_transform_parameters,
+    )
+
+
+def transform_annotation_image(
+    annotation_image: npt.NDArray[np.uint32],
+    transform_parameters: itk.ParameterObject,
+) -> npt.NDArray[np.uint32]:
+    """
+    Transform the annotation image using the given transform parameters.
+    Sets the FinalBSplineInterpolationOrder to 0 to avoid interpolation.
+    Resets the FinalBSplineInterpolationOrder to its original value after
+    transforming the annotation image.
+
+    Parameters
+    ----------
+    annotation_image : npt.NDArray
+        The annotation image.
+    transform_parameters : itk.ParameterObject
+        The transform parameters.
+
+    Returns
+    -------
+    npt.NDArray
+        The transformed annotation image.
+    """
+    adjusted_annotation_image, mapping = convert_atlas_labels(annotation_image)
+
+    annotation_image = itk.GetImageFromArray(adjusted_annotation_image).astype(
+        itk.F
+    )
+    temp_interp_order = transform_parameters.GetParameter(
         0, "FinalBSplineInterpolationOrder"
     )
-    result_transform_parameters.SetParameter(
-        "FinalBSplineInterpolationOrder", "0"
+    transform_parameters.SetParameter("FinalBSplineInterpolationOrder", "0")
+
+    transformix_object = itk.TransformixFilter.New(annotation_image)
+    transformix_object.SetTransformParameterObject(transform_parameters)
+    transformix_object.UpdateLargestPossibleRegion()
+
+    transformed_annotation = transformix_object.GetOutput()
+
+    transform_parameters.SetParameter(
+        "FinalBSplineInterpolationOrder", temp_interp_order
+    )
+    transformed_annotation_array = np.asarray(transformed_annotation).astype(
+        np.uint32
     )
 
-    annotation_image_transformix = itk.transformix_filter(
-        annotation_image.astype(np.float32, copy=False),
-        result_transform_parameters,
+    transformed_annotation_array = restore_atlas_labels(
+        transformed_annotation_array, mapping
+    )
+
+    return transformed_annotation_array
+
+
+def transform_image(
+    image: npt.NDArray,
+    transform_parameters: itk.ParameterObject,
+) -> npt.NDArray:
+    """
+    Transform the image using the given transform parameters.
+
+    Parameters
+    ----------
+    image: npt.NDArray
+        The image to transform.
+    transform_parameters: itk.ParameterObject
+        The transform parameters.
+
+    Returns
+    -------
+    npt.NDArray
+        The transformed image.
+    """
+    image = itk.GetImageViewFromArray(image).astype(itk.F)
+
+    transformix_object = itk.TransformixFilter.New(image)
+    transformix_object.SetTransformParameterObject(transform_parameters)
+    transformix_object.UpdateLargestPossibleRegion()
+
+    transformed_image = transformix_object.GetOutput()
+
+    return np.asarray(transformed_image)
+
+
+def calculate_deformation_field(
+    moving_image: npt.NDArray,
+    transform_parameters: itk.ParameterObject,
+) -> npt.NDArray:
+    """
+    Calculate the deformation field for the moving image using the given
+    transform parameters.
+
+    Parameters
+    ----------
+    moving_image : npt.NDArray
+        The moving image.
+    transform_parameters : itk.ParameterObject
+        The transform parameters.
+
+    Returns
+    -------
+    npt.NDArray
+        The deformation field.
+    """
+    transformix_object = itk.TransformixFilter.New(
+        itk.GetImageViewFromArray(moving_image).astype(itk.F),
+        transform_parameters,
+    )
+    transformix_object.SetComputeDeformationField(True)
+
+    transformix_object.UpdateLargestPossibleRegion()
+
+    # Change from ITK to numpy axes ordering
+    deformation_field = itk.GetArrayFromImage(
+        transformix_object.GetOutputDeformationField()
+    )[..., ::-1]
+
+    # Cleanup files generated by elastix
+    (Path.cwd() / "DeformationField.tiff").unlink(missing_ok=True)
+
+    return deformation_field
+
+
+def invert_transformation(
+    fixed_image: npt.NDArray,
+    parameter_list: List[Tuple[str, dict]],
+    transform_parameters: itk.ParameterObject,
+    output_directory: Optional[Path] = None,
+) -> itk.ParameterObject:
+
+    fixed_image = itk.GetImageFromArray(fixed_image).astype(itk.F)
+
+    elastix_object = itk.ElastixRegistrationMethod.New(
+        fixed_image, fixed_image
     )
 
+    parameter_object_inverse = setup_parameter_object(parameter_list)
+
+    elastix_object.SetInitialTransformParameterObject(transform_parameters)
+
+    elastix_object.SetParameterObject(parameter_object_inverse)
+
+    elastix_object.UpdateLargestPossibleRegion()
+
+    num_initial_transforms = transform_parameters.GetNumberOfParameterMaps()
+
+    result_image = elastix_object.GetOutput()
+    out_parameters = elastix_object.GetTransformParameterObject()
+    result_transform_parameters = itk.ParameterObject.New()
+
+    for i in range(
+        num_initial_transforms, out_parameters.GetNumberOfParameterMaps()
+    ):
+        result_transform_parameters.AddParameterMap(
+            out_parameters.GetParameterMap(i)
+        )
+
     result_transform_parameters.SetParameter(
-        "FinalBSplineInterpolationOrder", temp_interp_order
+        0, "InitialTransformParameterFileName", "NoInitialTransform"
     )
 
+    if output_directory:
+        file_names = [
+            f"{output_directory}/InverseTransformParameters.{i}.txt"
+            for i in range(len(parameter_list))
+        ]
+
+        itk.ParameterObject.WriteParameterFiles(
+            result_transform_parameters, file_names
+        )
+
     return (
         np.asarray(result_image),
         result_transform_parameters,
-        np.asarray(annotation_image_transformix),
     )
 
 

brainglobe_registration/parameters/ara_tools/affine.txt

@@ -4,9 +4,9 @@
 
 //ImageTypes
 (FixedInternalImagePixelType "float")
-(FixedImageDimension 3)
+(FixedImageDimension 2)
 (MovingInternalImagePixelType "float")
-(MovingImageDimension 3)
+(MovingImageDimension 2)
 
 //Components
 (Registration "MultiResolutionRegistration")

brainglobe_registration/parameters/ara_tools/bspline.txt

@@ -2,9 +2,9 @@
 
 //ImageTypes
 (FixedInternalImagePixelType "float")
-(FixedImageDimension 3)
+(FixedImageDimension 2)
 (MovingInternalImagePixelType "float")
-(MovingImageDimension 3)
+(MovingImageDimension 2)
 
 //Components
 (Registration "MultiResolutionRegistration")

brainglobe_registration/parameters/elastix_default/affine.txt

@@ -18,7 +18,7 @@
 (Registration "MultiResolutionRegistration")
 (ResampleInterpolator "FinalBSplineInterpolator")
 (Resampler "DefaultResampler")
-(ResultImageFormat "nii")
+(ResultImageFormat "tiff")
 (Transform "AffineTransform")
 (WriteIterationInfo "false")
 (WriteResultImage "true")

brainglobe_registration/parameters/elastix_default/bspline.txt

@@ -21,7 +21,7 @@
 (Registration "MultiMetricMultiResolutionRegistration")
 (ResampleInterpolator "FinalBSplineInterpolator")
 (Resampler "DefaultResampler")
-(ResultImageFormat "nii")
+(ResultImageFormat "tiff")
 (Transform "BSplineTransform")
 (WriteIterationInfo "false")
 (WriteResultImage "true")