Feature/nightly

.github/workflows/nightly-health.yml

@@ -58,7 +58,7 @@ jobs:
         # Python 3.11 is required because the [physicsnemo] extra pulls in
         # nvidia-physicsnemo, which requires Python >= 3.11.
         run: |
-          & "C:\Users\saylward\AppData\Local\Programs\Python\Python311\python.exe" -m venv "$env:RUNNER_TEMP\physiomotion4d-venv"
+          & py -m venv "$env:RUNNER_TEMP\physiomotion4d-venv"
           echo "$env:RUNNER_TEMP\physiomotion4d-venv\Scripts" >> $env:GITHUB_PATH
 
       - name: Cache uv packages

README.md

@@ -159,7 +159,7 @@ print(WorkflowConvertImageToUSD.__name__)
 
 ### Key Dependencies
 
-- **Medical Imaging**: ITK, TubeTK, MONAI, nibabel, PyVista
+- **Medical Imaging**: ITK, MONAI, nibabel, PyVista
 - **AI/ML**: PyTorch, CuPy (CUDA 13), transformers, MONAI
 - **Registration**: icon-registration, unigradicon
 - **Visualization**: USD-core, PyVista

docs/conf.py

@@ -26,7 +26,6 @@ def __getattr__(cls, name):
 
 
 # Mock modules that need special handling
-sys.modules["itk.TubeTK"] = Mock()
 sys.modules["icon_registration.losses"] = Mock()
 sys.modules["icon_registration.network_wrappers"] = Mock()
 

docs/installation.rst

@@ -21,7 +21,7 @@ Software Dependencies
 
 PhysioMotion4D relies on several key packages:
 
-* **Medical Imaging**: ITK, TubeTK, MONAI, nibabel, PyVista
+* **Medical Imaging**: ITK, MONAI, nibabel, PyVista
 * **AI/ML**: PyTorch, CuPy (CUDA 13), transformers, MONAI
 * **Registration**: icon-registration, unigradicon
 * **Visualization**: USD-core, PyVista

experiments/Heart-Statistical_Model_To_Patient/heart_model_to_model_icp_itk.py

@@ -22,14 +22,14 @@
 import itk
 import numpy as np
 import pyvista as pv
-from itk import TubeTK as ttk
 
 # Import from PhysioMotion4D package
 from physiomotion4d import (
     ContourTools,
     RegisterModelsICPITK,
     TransformTools,
 )
+from physiomotion4d.image_tools import ImageTools
 from physiomotion4d.test_tools import TestTools
 
 # %% [markdown]
@@ -64,10 +64,7 @@
 
 # Resample to 1mm isotropic spacing
 print("Resampling to sotropic...")
-resampler = ttk.ResampleImage.New(Input=patient_image)
-resampler.SetMakeHighResIso(True)
-resampler.Update()
-patient_image = resampler.GetOutput()
+patient_image = ImageTools().make_isotropic_image(patient_image)
 
 print(f"  Resampled size: {itk.size(patient_image)}")
 print(f"  Resampled spacing: {itk.spacing(patient_image)}")

experiments/Heart-Statistical_Model_To_Patient/heart_model_to_model_registration_pca.py

@@ -24,7 +24,6 @@
 import itk
 import numpy as np
 import pyvista as pv
-from itk import TubeTK as ttk
 
 # Import from PhysioMotion4D package
 from physiomotion4d import (
@@ -33,6 +32,7 @@
     RegisterModelsPCA,
     TransformTools,
 )
+from physiomotion4d.image_tools import ImageTools
 from physiomotion4d.test_tools import TestTools
 
 # %% [markdown]
@@ -75,10 +75,7 @@
 
 # Resample to 1mm isotropic spacing
 print("Resampling to sotropic...")
-resampler = ttk.ResampleImage.New(Input=patient_image)
-resampler.SetMakeHighResIso(True)
-resampler.Update()
-patient_image = resampler.GetOutput()
+patient_image = ImageTools().make_isotropic_image(patient_image)
 
 print(f"  Resampled size: {itk.size(patient_image)}")
 print(f"  Resampled spacing: {itk.spacing(patient_image)}")

experiments/Lung-GatedCT_To_USD/0-register_dirlab_4dct.py

@@ -6,8 +6,8 @@
 import itk
 import numpy as np
 from data_dirlab_4d_ct import DataDirLab4DCT
-from itk import TubeTK as tube
 
+from physiomotion4d.image_tools import ImageTools
 from physiomotion4d.register_images_icon import RegisterImagesICON
 from physiomotion4d.segment_chest_total_segmentator import SegmentChestTotalSegmentator
 from physiomotion4d.transform_tools import TransformTools
@@ -33,10 +33,7 @@
     # %%
     def dilate_mask(mask: Optional[itk.image], dilation: int) -> Optional[itk.image]:
         if mask is not None:
-            im_math = tube.ImageMath.New(mask)
-            im_math.Dilate(dilation, 1, 0)
-            dilated_mask = im_math.GetOutputShort()
-            return dilated_mask
+            return ImageTools().binary_dilate_image(mask, dilation, 1, 0)
         return None
 
     def register_image(

pyproject.toml

@@ -55,7 +55,6 @@ keywords = [
 dependencies = [
     # Core medical imaging
     "itk>=5.3.0",
-    "itk-tubetk>=1.4.0",
     "nibabel>=4.0.0",
     "numpy>=1.21.0",
     "pydicom>=2.4.0",

src/physiomotion4d/image_tools.py

@@ -234,9 +234,6 @@ def convert_array_to_image_of_vectors(
     def make_isotropic_image(self, image: itk.Image) -> itk.Image:
         """Resample a 3-D *image* to isotropic spacing using the finest voxel pitch.
 
-        Equivalent to TubeTK's ResampleImage.SetMakeHighResIso(True), implemented
-        with standard ITK ResampleImageFilter so that TubeTK is not needed here.
-
         Args:
             image: 3-D ITK image to resample.
 
@@ -273,6 +270,118 @@ def make_isotropic_image(self, image: itk.Image) -> itk.Image:
         result.DisconnectPipeline()
         return result
 
+    def binary_dilate_image(
+        self,
+        image: itk.Image,
+        radius: int,
+        foreground_value: int = 1,
+        background_value: int = 0,
+    ) -> itk.Image:
+        """Binary-dilate *image* with a ball structuring element.
+
+        Args:
+            image: Binary (or label) image to dilate.
+            radius: Radius, in voxels, of the ball structuring element.
+            foreground_value: Pixel value treated as foreground (default: 1).
+            background_value: Pixel value written for background voxels
+                (default: 0).
+
+        Returns:
+            Dilated image with the same pixel type as *image*.
+        """
+        ImageType = type(image)
+        dimension = image.GetImageDimension()
+        StructuringElementType = itk.FlatStructuringElement[dimension]
+        structuring_element = StructuringElementType.Ball(int(radius))
+
+        dilate_filter = itk.BinaryDilateImageFilter[
+            ImageType, ImageType, StructuringElementType
+        ].New()
+        dilate_filter.SetInput(image)
+        dilate_filter.SetKernel(structuring_element)
+        dilate_filter.SetForegroundValue(foreground_value)
+        dilate_filter.SetBackgroundValue(background_value)
+        dilate_filter.Update()
+        result = dilate_filter.GetOutput()
+        result.DisconnectPipeline()
+        return result
+
+    def binary_erode_image(
+        self,
+        image: itk.Image,
+        radius: int,
+        foreground_value: int = 1,
+        background_value: int = 0,
+    ) -> itk.Image:
+        """Binary-erode *image* with a ball structuring element.
+
+        Args:
+            image: Binary (or label) image to erode.
+            radius: Radius, in voxels, of the ball structuring element.
+            foreground_value: Pixel value treated as foreground (default: 1).
+            background_value: Pixel value written for eroded-away voxels
+                (default: 0).
+
+        Returns:
+            Eroded image with the same pixel type as *image*.
+        """
+        ImageType = type(image)
+        dimension = image.GetImageDimension()
+        StructuringElementType = itk.FlatStructuringElement[dimension]
+        structuring_element = StructuringElementType.Ball(int(radius))
+
+        erode_filter = itk.BinaryErodeImageFilter[
+            ImageType, ImageType, StructuringElementType
+        ].New()
+        erode_filter.SetInput(image)
+        erode_filter.SetKernel(structuring_element)
+        erode_filter.SetForegroundValue(foreground_value)
+        erode_filter.SetBackgroundValue(background_value)
+        erode_filter.Update()
+        result = erode_filter.GetOutput()
+        result.DisconnectPipeline()
+        return result
+
+    def keep_largest_connected_component(
+        self,
+        image: itk.Image,
+        foreground_value: int = 1,
+        fully_connected: bool = False,
+    ) -> itk.Image:
+        """Keep only the largest connected component of a binary image.
+
+        Args:
+            image: Binary (non-zero = foreground) image.
+            foreground_value: Value written for the retained component's
+                voxels (default: 1).
+            fully_connected: Whether diagonally-adjacent voxels are
+                considered connected (default: False, i.e. face connectivity
+                only).
+
+        Returns:
+            Binary image, same pixel type as *image*, containing only the
+            largest connected component with value *foreground_value*
+            (background is 0).
+        """
+        ImageType = type(image)
+
+        connected_component_image = itk.connected_component_image_filter(
+            image, fully_connected=fully_connected
+        )
+        relabeled_image = itk.relabel_component_image_filter(
+            connected_component_image, sort_by_object_size=True
+        )
+        result = itk.binary_threshold_image_filter(
+            Input=relabeled_image,
+            LowerThreshold=1,
+            UpperThreshold=1,
+            InsideValue=foreground_value,
+            OutsideValue=0,
+        )
+        if type(result) is not ImageType:
+            result = itk.cast_image_filter(result, ttype=(type(result), ImageType))
+        return result
+
     @overload
     def flip_image(
         self,

src/physiomotion4d/register_images_icon.py

@@ -24,8 +24,7 @@
 
 def _load_icon():
     """Lazy-load icon_registration, torch, and unigradicon to avoid
-    initializing GPU/CUDA resources at import time, which interferes with
-    TubeTK's memory allocator on Windows."""
+    initializing GPU/CUDA resources at import time."""
     import icon_registration as icon
     import icon_registration.itk_wrapper
     import torch

src/physiomotion4d/segment_anatomy_base.py

@@ -10,9 +10,9 @@
 
 import itk
 import numpy as np
-from itk import TubeTK as tube
 
 from .anatomy_taxonomy import AnatomyTaxonomy
+from .image_tools import ImageTools
 from .physiomotion4d_base import PhysioMotion4DBase
 
 
@@ -427,10 +427,13 @@ def segment_connected_component(
             InsideValue=1,
             OutsideValue=0,
         )
-        imMath = tube.ImageMath.New(connected_component_image)
-        imMath.Dilate(hole_fill, 1, 0)
-        imMath.Erode(hole_fill, 1, 0)
-        connected_component_image = imMath.GetOutputUChar()
+        image_tools = ImageTools()
+        connected_component_image = image_tools.binary_dilate_image(
+            connected_component_image, hole_fill, 1, 0
+        )
+        connected_component_image = image_tools.binary_erode_image(
+            connected_component_image, hole_fill, 1, 0
+        )
 
         labelmap_arr = itk.GetArrayFromImage(labelmap_image)
         connected_component_arr = itk.GetArrayFromImage(connected_component_image)

src/physiomotion4d/segment_heart_simpleware.py

@@ -15,8 +15,8 @@
 
 import itk
 import numpy as np
-from itk import TubeTK as tube
 
+from .image_tools import ImageTools
 from .segment_anatomy_base import SegmentAnatomyBase
 
 
@@ -304,11 +304,14 @@ def segmentation_method(self, preprocessed_image: itk.image) -> itk.image:
             # Dilate the interior regions to simulate 3mm myocardium (heart)
             interior_image = itk.GetImageFromArray(interior_array.astype(np.uint8))
             interior_image.CopyInformation(preprocessed_image)
-            imMath = tube.ImageMath.New(interior_image)
+            imMath = ImageTools()
             spacing = interior_image.GetSpacing()
-            imMath.Dilate(round(7 / spacing[0]), 1, 0)
-            imMath.Erode(round(4 / spacing[0]), 1, 0)
-            exterior_image = imMath.GetOutputUChar()
+            exterior_image = imMath.binary_dilate_image(
+                interior_image, round(7 / spacing[0]), 1, 0
+            )
+            exterior_image = imMath.binary_erode_image(
+                exterior_image, round(4 / spacing[0]), 1, 0
+            )
             exterior_array = itk.GetArrayFromImage(exterior_image)
             mask_id = 6  # Heart mask id
             exterior_array = exterior_array * mask_id