ENH: Simplify heart models for inter-patient consistency

experiments/Convert_VTK_To_USD/convert_chop_valve_to_usd.ipynb

@@ -1132,4 +1132,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

experiments/Heart-Simpleware_Segmentation/simpleware_heart_segmentation.ipynb

@@ -367,6 +367,9 @@
     "    heart_array = itk.array_from_image(result[\"heart\"])\n",
     "    vessels_array = itk.array_from_image(result[\"major_vessels\"])\n",
     "\n",
+    "    labelmap_essentials = segmenter.trim_mask_to_essentials(result[\"labelmap\"])\n",
+    "    labelmap_essentials_array = itk.array_from_image(labelmap_essentials)\n",
+    "\n",
     "    # Select middle slice\n",
     "    mid_slice = image_array.shape[0] // 2\n",
     "\n",
@@ -380,7 +383,8 @@
     "\n",
     "    axes[0, 1].imshow(image_array[mid_slice, :, :], cmap=\"gray\", vmin=-200, vmax=400)\n",
     "    labelmap_overlay = np.ma.masked_where(\n",
-    "        labelmap_array[mid_slice, :, :] == 0, labelmap_array[mid_slice, :, :]\n",
+    "        labelmap_essentials_array[mid_slice, :, :] == 0,\n",
+    "        labelmap_essentials_array[mid_slice, :, :],\n",
     "    )\n",
     "    axes[0, 1].imshow(labelmap_overlay, cmap=\"jet\", alpha=0.5, vmin=1, vmax=10)\n",
     "    axes[0, 1].set_title(\"Labelmap Overlay\")\n",
@@ -407,7 +411,8 @@
     "    mid_sagittal = image_array.shape[2] // 2\n",
     "    axes[1, 1].imshow(image_array[:, :, mid_sagittal], cmap=\"gray\", vmin=-200, vmax=400)\n",
     "    sagittal_overlay = np.ma.masked_where(\n",
-    "        labelmap_array[:, :, mid_sagittal] == 0, labelmap_array[:, :, mid_sagittal]\n",
+    "        labelmap_essentials_array[:, :, mid_sagittal] == 0,\n",
+    "        labelmap_essentials_array[:, :, mid_sagittal],\n",
     "    )\n",
     "    axes[1, 1].imshow(sagittal_overlay, cmap=\"jet\", alpha=0.5, vmin=1, vmax=10)\n",
     "    axes[1, 1].set_title(\"Sagittal View\")\n",
@@ -417,7 +422,8 @@
     "    mid_coronal = image_array.shape[1] // 2\n",
     "    axes[1, 2].imshow(image_array[:, mid_coronal, :], cmap=\"gray\", vmin=-200, vmax=400)\n",
     "    coronal_overlay = np.ma.masked_where(\n",
-    "        labelmap_array[:, mid_coronal, :] == 0, labelmap_array[:, mid_coronal, :]\n",
+    "        labelmap_essentials_array[:, mid_coronal, :] == 0,\n",
+    "        labelmap_essentials_array[:, mid_coronal, :],\n",
     "    )\n",
     "    axes[1, 2].imshow(coronal_overlay, cmap=\"jet\", alpha=0.5, vmin=1, vmax=10)\n",
     "    axes[1, 2].set_title(\"Coronal View\")\n",
@@ -457,6 +463,7 @@
     "\n",
     "# Convert heart mask to VTK\n",
     "heart_vtk = itk.vtk_image_from_image(result[\"heart\"])\n",
+    "heart_essentials_vtk = itk.vtk_image_from_image(labelmap_essentials)\n",
     "vessels_vtk = itk.vtk_image_from_image(result[\"major_vessels\"])\n",
     "\n",
     "# Create PyVista plotter\n",
@@ -466,7 +473,15 @@
     "heart_grid = pv.wrap(heart_vtk)\n",
     "heart_surface = heart_grid.contour([0.5])\n",
     "if heart_surface.n_points > 0:\n",
-    "    plotter.add_mesh(heart_surface, color=\"red\", opacity=1.0, label=\"Heart\")\n",
+    "    plotter.add_mesh(heart_surface, color=\"red\", opacity=0.5, label=\"Heart\")\n",
+    "\n",
+    "# Extract heart surface\n",
+    "heart_essentials_grid = pv.wrap(heart_essentials_vtk)\n",
+    "heart_essentials_surface = heart_essentials_grid.contour([0.5])\n",
+    "if heart_essentials_surface.n_points > 0:\n",
+    "    plotter.add_mesh(\n",
+    "        heart_essentials_surface, color=\"grey\", opacity=1.0, label=\"Heart Essential\"\n",
+    "    )\n",
     "\n",
     "# Extract vessels surface\n",
     "vessels_grid = pv.wrap(vessels_vtk)\n",
@@ -486,6 +501,14 @@
     "print(f\"3D visualization saved to: {screenshot_path}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6046393",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "o3p4q5r6",

pyproject.toml

@@ -65,7 +65,7 @@ dependencies = [
     # AI/ML and segmentation
     "monai>=1.3.0",
     "torch>=2.0.0,<3.0.0",
-    "transformers>=4.21.0",
+    "transformers>=4.21.0,<5.0.0",
     "totalsegmentator>=2.0.0",
 
     # Registration

src/physiomotion4d/cli/fit_statistical_model_to_patient.py

@@ -234,6 +234,8 @@ def main() -> int:
                 pca_model=pca_model,
                 pca_number_of_modes=args.pca_number_of_modes,
             )
+        if args.use_mask_to_mask:
+            workflow.set_use_mask_to_mask_registration(args.use_mask_to_mask)
         if args.use_mask_to_image:
             workflow.set_use_mask_to_image_registration(
                 True,
@@ -252,8 +254,6 @@ def main() -> int:
         print("\nStarting registration pipeline...")
         print("=" * 70)
         result = workflow.run_workflow(
-            use_mask_to_mask_registration=args.use_mask_to_mask,
-            use_mask_to_image_registration=args.use_mask_to_image,
             use_icon_registration_refinement=args.use_icon_refinement,
         )
 

src/physiomotion4d/cli/visualize_pca_modes.py

@@ -140,8 +140,11 @@ def main() -> int:
         traceback.print_exc()
         return 1
 
-    if not isinstance(mean_mesh, pv.PolyData):
-        print("Error: PCA mean surface must be a PolyData (.vtp).")
+    if not isinstance(mean_mesh, (pv.PolyData, pv.UnstructuredGrid)):
+        print(
+            "Error: PCA mean surface must be PolyData or UnstructuredGrid.",
+            f"Type: {type(mean_mesh)}",
+        )
         return 1
 
     try:

src/physiomotion4d/contour_tools.py

@@ -165,12 +165,15 @@ def create_reference_image(
 
     def create_mask_from_mesh(
         self,
-        mesh: pv.DataSet,
+        mesh: pv.DataSet | pv.UnstructuredGrid,
         reference_image: itk.Image,
     ) -> itk.Image:
         ref_spacing = np.array(reference_image.GetSpacing())
 
         # Create trimesh object with LPS coordinates
+        if isinstance(mesh, pv.UnstructuredGrid):
+            mesh = mesh.extract_surface()
+
         if hasattr(mesh, "n_faces_strict"):
             # PyVista PolyData
             faces = mesh.faces.reshape((mesh.n_faces_strict, 4))[:, 1:]
@@ -248,7 +251,7 @@ def create_mask_from_mesh(
 
     def create_distance_map(
         self,
-        mesh: pv.DataSet,
+        mesh: pv.DataSet | pv.UnstructuredGrid,
         reference_image: itk.Image,
         squared_distance: bool = False,
         negative_inside: bool = True,

src/physiomotion4d/segment_heart_simpleware.py

@@ -98,7 +98,7 @@ def __init__(self, log_level: int | str = logging.INFO):
         # From Base Class
         # self.contrast_mask_ids = {135: "contrast"}
 
-        self.trim_mesh_to_essentials = False
+        self._trim_mask = False
 
         self.set_other_and_all_mask_ids()
 
@@ -112,13 +112,13 @@ def __init__(self, log_level: int | str = logging.INFO):
             "SimplewareScript_heart_segmentation.py",
         )
 
-    def set_trim_mesh_to_essentials(self, trim_mesh_to_essentials: bool) -> None:
-        """Set whether to trim mesh to common and critical structures.
+    def set_trim_mask_to_essentials(self, trim_mask: bool) -> None:
+        """Set whether to trim mask to common and critical structures.
 
         Args:
-            trim_mesh_to_essentials (bool): Whether to reduce to essential.
+            trim_mask (bool): Whether to reduce to essential.
         """
-        self.trim_mesh_to_essentials = trim_mesh_to_essentials
+        self._trim_mask = trim_mask
 
     def set_simpleware_executable_path(self, path: str) -> None:
         """Set the path to the Simpleware Medical console executable.
@@ -283,8 +283,9 @@ def segmentation_method(self, preprocessed_image: itk.image) -> itk.image:
             interior_image = itk.GetImageFromArray(interior_array.astype(np.uint8))
             interior_image.CopyInformation(preprocessed_image)
             imMath = tube.ImageMath.New(interior_image)
-            imMath.Dilate(7, 1, 0)
-            imMath.Erode(4, 1, 0)
+            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_array = itk.GetArrayFromImage(exterior_image)
             mask_id = 6  # Heart mask id
@@ -300,17 +301,144 @@ def segmentation_method(self, preprocessed_image: itk.image) -> itk.image:
                     "ensure the ASCardio module ran successfully."
                 )
 
-            if self.trim_mesh_to_essentials:
-                z = labelmap_array.shape[2] - 1
-                z_classes = np.unique(labelmap_array[z, :, :])
-                heart_count = np.sum((c in [1, 2, 3, 4, 5]) for c in z_classes)
-                while heart_count < 3 and z > 0:
-                    z -= 1
-                    z_classes = np.unique(labelmap_array[z, :, :])
-                    heart_count = np.sum((c in [1, 2, 3, 4, 5]) for c in z_classes)
-                if z < labelmap_array.shape[2] - 3:
-                    labelmap_array[(z + 3) :, :, :] = 0
             labelmap_image = itk.GetImageFromArray(labelmap_array.astype(np.uint8))
             labelmap_image.CopyInformation(preprocessed_image)
 
+            if self._trim_mask:
+                labelmap_image = self.trim_mask_to_essentials(labelmap_image)
+
         return labelmap_image
+
+    def trim_mask_to_essentials(self, labelmap_image: itk.image) -> itk.image:
+        """Trim mask to essentials."""
+
+        # Reference code for cropping aorta and pulmonary artery to
+        #    portions adjacent to the heart.
+        # Trim z-axis
+        # z = labelmap_array.shape[2] - 1
+        # z_classes = np.unique(labelmap_array[z, :, :])
+        # heart_count = np.sum((c in [1, 2, 3, 4, 5]) for c in z_classes)
+        # while heart_count < 3 and z > 0:
+        #     z -= 1
+        #     z_classes = np.unique(labelmap_array[z, :, :])
+        #     heart_count = np.sum((c in [1, 2, 3, 4, 5]) for c in z_classes)
+        # if z < labelmap_array.shape[2] - 3:
+        # labelmap_array[(z + 3) :, :, :] = 0
+
+        # In labelmap,
+        #  if pixel is in keep_mask, was left or right atrium, then keep as
+        #     left or right atrium
+
+        #  1) Erase Heart and Myo label
+        labelmap_arr = itk.array_from_image(labelmap_image)
+
+        heart_arr = itk.array_from_image(labelmap_image)
+        heart_arr[heart_arr == 6] = 0
+        heart_arr[heart_arr == 5] = 0
+
+        img = itk.image_from_array(heart_arr)
+        img.CopyInformation(labelmap_image)
+        imMath = tube.ImageMath.New(img)
+
+        #  2) Erode then Dilate Left Atrium label to clip vessels
+        spacing = labelmap_image.GetSpacing()
+        imMath.Erode(round(7 / spacing[0]), 3, 0)
+        imMath.Dilate(round(7 / spacing[0]), 3, 0)
+
+        #  3) Erode then Dilate Right Atrium label to clip vessels
+        imMath.Erode(round(7 / spacing[0]), 4, 0)
+        imMath.Dilate(round(7 / spacing[0]), 4, 0)
+        simple_img = imMath.GetOutput()
+        simple_arr = itk.array_from_image(simple_img)
+
+        #  Keep the largest component of the left atrium
+        simple_arr_3 = simple_arr.copy()
+        simple_arr_3[simple_arr_3 != 3] = 0
+        simple_arr_3[simple_arr_3 == 3] = 1
+        simple_img_3 = itk.image_from_array(simple_arr_3)
+        connComp = tube.SegmentConnectedComponents.New(simple_img_3)
+        connComp.SetKeepOnlyLargestComponent(True)
+        connComp.Update()
+        mask_img_3 = connComp.GetOutput()
+        mask_arr_3 = itk.array_from_image(mask_img_3)
+        simple_arr_3[mask_arr_3 == 0] = 0
+
+        #  Keep the largest component of the right atrium
+        simple_arr_4 = simple_arr.copy()
+        simple_arr_4[simple_arr_4 != 4] = 0
+        simple_arr_4[simple_arr_4 == 4] = 1
+        simple_img_4 = itk.image_from_array(simple_arr_4)
+        connComp = tube.SegmentConnectedComponents.New(simple_img_4)
+        connComp.SetKeepOnlyLargestComponent(True)
+        connComp.Update()
+        mask_img_4 = connComp.GetOutput()
+        mask_arr_4 = itk.array_from_image(mask_img_4)
+        simple_arr_4[mask_arr_4 == 0] = 0
+
+        #  Replace the left and right atrium labels with the largest components
+        simple_arr[simple_arr == 3] = 0
+        simple_arr[simple_arr == 4] = 0
+        simple_arr[simple_arr_3 > 0] = 3
+        simple_arr[simple_arr_4 > 0] = 4
+        simple_img = itk.image_from_array(simple_arr)
+        simple_img.CopyInformation(labelmap_image)
+
+        #  4) Dilate all others = keep_mask
+        keep_mask_arr = heart_arr.copy()
+        keep_mask_arr[keep_mask_arr == 2] = 1
+        keep_mask_arr[keep_mask_arr == 5] = 1
+        keep_mask_arr[keep_mask_arr != 1] = 0
+        keep_mask = itk.image_from_array(keep_mask_arr)
+        keep_mask.CopyInformation(labelmap_image)
+        imMath.SetInput(keep_mask)
+        imMath.Dilate(round(7 / spacing[0]), 1, 0)
+        keep_mask = imMath.GetOutput()
+        keep_mask_arr = itk.array_from_image(keep_mask)
+
+        #  Add the left and right atrium labels to the keep_mask
+        heart_arr = heart_arr * keep_mask_arr
+        heart_arr[simple_arr == 3] = 3
+        heart_arr[simple_arr == 4] = 4
+        heart_img = itk.image_from_array(heart_arr)
+        heart_img.CopyInformation(labelmap_image)
+
+        #  Dilate the keep_mask to simulate 3mm (heart)
+        keep_mask_arr = heart_arr.copy()
+        keep_mask_arr[keep_mask_arr == 1] = 0
+        keep_mask_arr[keep_mask_arr > 0] = 1
+        keep_mask = itk.image_from_array(keep_mask_arr)
+        keep_mask.CopyInformation(labelmap_image)
+        imMath.SetInput(keep_mask)
+        imMath.Dilate(round(5 / spacing[0]), 1, 0)
+        imMath.Erode(round(2 / spacing[0]), 1, 0)
+        heart_mask = imMath.GetOutput()
+
+        #  Insert the heart and myo labels back into the labelmap
+        heart_mask_arr = itk.array_from_image(heart_mask)
+        heart_mask_arr[heart_arr > 0] = 0
+        heart_arr[heart_mask_arr > 0] = 6
+        heart_arr_myo = itk.array_from_image(labelmap_image)
+        heart_arr[heart_arr_myo == 5] = 5
+        heart_arr[heart_arr_myo == 1] = 1
+        heart_img = itk.image_from_array(heart_arr)
+        heart_img.CopyInformation(labelmap_image)
+
+        #  Add in missing pieces / gaps of the myocardium
+        lv_arr = heart_arr.copy()
+        lv_arr[lv_arr != 1] = 0
+        lv_img = itk.image_from_array(lv_arr)
+        lv_img.CopyInformation(labelmap_image)
+        imMath.SetInput(lv_img)
+        imMath.Dilate(round(2 / spacing[0]), 1, 0)
+        lv_img = imMath.GetOutput()
+        lv_arr = itk.array_from_image(lv_img)
+        lv_arr = lv_arr * 5  # Myocardium label is 5
+
+        #  Add the gap-filled myocardium back into the labelmap
+        heart_arr = np.where(heart_arr == 0, lv_arr, heart_arr)
+        # Eliminate overlap with other labels
+        heart_arr = np.where(labelmap_arr > 6, 0, heart_arr)
+        heart_img = itk.image_from_array(heart_arr)
+        heart_img.CopyInformation(labelmap_image)
+
+        return heart_img