From e0bba334154f58be532361cddc3b929b0f8d01d4 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 12 Dec 2024 09:22:23 +0800 Subject: [PATCH] From 3D Segmentation to Omniverse: Mesh Conversion, USD Export, and Visualization Workflow (#1891) Fixes # . ### Description This tutorial focuses on converting raw segmentation data into USD format and visualizing it in NVIDIA Omniverse for high-quality 3D rendering and manipulation. ### Checks - [ ] Avoid including large-size files in the PR. - [ ] Clean up long text outputs from code cells in the notebook. - [ ] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder - [ ] Notebook runs automatically `./runner.sh -t ` --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- modules/omniverse/Spleen.png | Bin 0 -> 231800 bytes modules/omniverse/omniverse.png | Bin 0 -> 752426 bytes modules/omniverse/omniverse_integration.ipynb | 605 ++++++++++++++++++ modules/omniverse/result.png | Bin 0 -> 1441638 bytes modules/omniverse/utility.py | 228 +++++++ runner.sh | 2 + 6 files changed, 835 insertions(+) create mode 100644 modules/omniverse/Spleen.png create mode 100644 modules/omniverse/omniverse.png create mode 100644 modules/omniverse/omniverse_integration.ipynb create mode 100644 modules/omniverse/result.png create mode 100644 modules/omniverse/utility.py diff --git a/modules/omniverse/Spleen.png b/modules/omniverse/Spleen.png new file mode 100644 index 0000000000000000000000000000000000000000..b3b60adfad0a8f881726e0b4b1ef47e48b14d169 GIT binary patch literal 231800 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znE&P0@c2NVH=e-8pF5lLV?$u&_q^&;=kYJMR<8#RGw)vooY*NlbC!8SWZ8XwSy`vu zM&Kd8$oE&3RZY)2K4frs-2)%Ti*a+bIPE0fq=AqzHr`egR?aJ9&RkFSsy z;~E*CxZoSJ?@Tc;mAtroe1GzFH)@zI2<@YSbyX90lxIeeYqJ}Sg8 zzvH2kE(Hh6$U;X<8A>F!ude?Ln&`dDj%rA_r<766lEsf98bM@(n5Xag1UFFO1qIKY z7YW1Iz*`j*;p6|nzU0dZFW>_Z3=c}6Jz%iv0xS6VIgpRnz3};BkjCYs)`5ZnJuulk igJoqAVa;%-<3GR0mD_SBw13 name: 'maisi_ct_generative'\n", + "2024-12-11 11:31:31,905 - INFO - > bundle_dir: '/workspace/Data'\n", + "2024-12-11 11:31:31,905 - INFO - > source: 'monaihosting'\n", + "2024-12-11 11:31:31,906 - INFO - > remove_prefix: 'monai_'\n", + "2024-12-11 11:31:31,906 - INFO - > progress: True\n", + "2024-12-11 11:31:31,906 - INFO - ---\n", + "\n", + "\n", + "2024-12-11 11:31:32,611 - INFO - Expected md5 is None, skip md5 check for file /workspace/Data/maisi_ct_generative_v0.4.5.zip.\n", + "2024-12-11 11:31:32,612 - INFO - File exists: /workspace/Data/maisi_ct_generative_v0.4.5.zip, skipped downloading.\n", + "2024-12-11 11:31:32,612 - INFO - Writing into directory: /workspace/Data.\n" + ] + } + ], + "source": [ + "download(name=\"maisi_ct_generative\", bundle_dir=root_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the Inference Workflow of the Bundle\n", + "We use the `create_workflow` API from MONAI to streamline the inference process directly from the bundle.\n", + "\n", + "Key input details for inference, such as the body region and target anatomy, are specified in [./configs/inference.json]. For a comprehensive explanation of the parameters, refer to [./docs/README.md] in the bundle directory. Additionally, we adjust the `output_size`, `spacing` and `num_splits` parameters to prevent out-of-memory issues during inference." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-12-11 11:32:14,010 - INFO - Setting logging properties based on config: /workspace/Data/maisi_ct_generative/configs/logging.conf.\n", + "2024-12-11 11:32:14,011 - py.warnings - WARNING - Detected deprecated name 'optional_packages_version' in configuration file, replacing with 'required_packages_version'.\n", + "\n", + "2024-12-11 11:32:14,021 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2024-12-11 11:32:14,022 - INFO - > workflow_type: 'inference'\n", + "2024-12-11 11:32:14,022 - INFO - > bundle_root: '/workspace/Data/maisi_ct_generative'\n", + "2024-12-11 11:32:14,022 - INFO - > output_size_xy: 256\n", + "2024-12-11 11:32:14,022 - INFO - > output_size_z: 256\n", + "2024-12-11 11:32:14,022 - INFO - > spacing_xy: 1.5\n", + "2024-12-11 11:32:14,023 - INFO - > spacing_z: 1.5\n", + "2024-12-11 11:32:14,023 - INFO - > autoencoder_def#num_splits: 16\n", + "2024-12-11 11:32:14,023 - INFO - > mask_generation_autoencoder_def#num_splits: 16\n", + "2024-12-11 11:32:14,023 - INFO - ---\n", + "\n", + "\n" + ] + } + ], + "source": [ + "bundle_root = os.path.join(root_dir, \"maisi_ct_generative\")\n", + "override = {\n", + " \"output_size_xy\": 256,\n", + " \"output_size_z\": 256,\n", + " \"spacing_xy\": 1.5,\n", + " \"spacing_z\": 1.5,\n", + " \"autoencoder_def#num_splits\": 16,\n", + " \"mask_generation_autoencoder_def#num_splits\": 16,\n", + "}\n", + "workflow = create_workflow(\n", + " config_file=os.path.join(bundle_root, \"configs/inference.json\"),\n", + " workflow_type=\"inference\",\n", + " bundle_root=bundle_root,\n", + " **override,\n", + ")\n", + "\n", + "# uncomment this line to run the inference workflow\n", + "# then you will get the generated CT images and paired masks which can be used for the following steps.\n", + "# In this tutorial, we just use the tested data (IntegrationTest-AbdomenCT.nii.gz) from bundle for demonstration.\n", + "# workflow.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert NIfTI/DICOM to Mesh and Save as OBJ/GLTF\n", + "\n", + "In this section, we convert the generated NII segmentation files into mesh format and save them as OBJ files. This process involves mapping labels to organs, exporting each organ as an individual mesh file, and generating a combined mesh file for all organs.\n", + "\n", + "We define a function `nii_to_mesh` to handle the conversion of NIfTI files to OBJ files. The workflow is as follows:\n", + "\n", + "- **Preprocessing**:\n", + "The function uses a series of transformations (`LoadImaged`, `BorderPadd`, and `SqueezeDimd`) to load and preprocess the input NIfTI file, ensuring it is ready for convertion.\n", + "\n", + "- **Organ Label Mapping**:\n", + "It iterates over a dictionary mapping organ names to their respective label values. For each organ:\n", + "A binary mask (single_organ) is created to isolate the organ by assigning its corresponding label value.\n", + "The segmented organ is saved as a NIfTI file.\n", + "\n", + "- **Mesh Conversion**:\n", + "Each segmented NIfTI file is converted into an OBJ file using the `convert_to_mesh` function.\n", + "\n", + "- **Combined Mesh**:\n", + "A combined segmentation file is created by merging all organ segmentations into a single NIfTI file. This file is then converted into a GLTF file, preserving the hierarchical structure of the organs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 17 groupings that cover 101 segments/regions out of 140\n", + "labels = {\n", + " \"Liver\": 1,\n", + " \"Spleen\": 3,\n", + " \"Pancreas\": 4,\n", + " \"Heart\": 115,\n", + " \"Body\": 200,\n", + " \"Gallbladder\": 10,\n", + " \"Stomach\": 12,\n", + " \"Small_bowel\": 19,\n", + " \"Colon\": 62,\n", + " \"Kidney\": {\"right_kidney\": 5, \"left_kidney\": 14},\n", + " \"Veins\": {\n", + " \"aorta\": 6,\n", + " \"inferior_vena_cava\": 7,\n", + " \"portal_vein_and_splenic_vein\": 17,\n", + " \"left_iliac_artery\": 58,\n", + " \"right_iliac_artery\": 59,\n", + " \"left_iliac_vena\": 60,\n", + " \"right_iliac_vena\": 61,\n", + " \"pulmonary_vein\": 119,\n", + " \"left_subclavian_artery\": 123,\n", + " \"right_subclavian_artery\": 124,\n", + " \"superior_vena_cava\": 125,\n", + " \"brachiocephalic_trunk\": 109,\n", + " \"left_brachiocephalic_vein\": 110,\n", + " \"right_brachiocephalic_vein\": 111,\n", + " \"left_common_carotid_artery\": 112,\n", + " \"right_common_carotid_artery\": 113,\n", + " },\n", + " \"Lungs\": {\n", + " \"left_lung_upper_lobe\": 28,\n", + " \"left_lung_lower_lobe\": 29,\n", + " \"right_lung_upper_lobe\": 30,\n", + " \"right_lung_middle_lobe\": 31,\n", + " \"right_lung_lower_lobe\": 32,\n", + " },\n", + " \"Spine\": {\n", + " \"vertebrae_L6\": 131,\n", + " \"vertebrae_L5\": 33,\n", + " \"vertebrae_L4\": 34,\n", + " \"vertebrae_L3\": 35,\n", + " \"vertebrae_L2\": 36,\n", + " \"vertebrae_L1\": 37,\n", + " \"vertebrae_T12\": 38,\n", + " \"vertebrae_T11\": 39,\n", + " \"vertebrae_T10\": 40,\n", + " \"vertebrae_T9\": 41,\n", + " \"vertebrae_T8\": 42,\n", + " \"vertebrae_T7\": 43,\n", + " \"vertebrae_T6\": 44,\n", + " \"vertebrae_T5\": 45,\n", + " \"vertebrae_T4\": 46,\n", + " \"vertebrae_T3\": 47,\n", + " \"vertebrae_T2\": 48,\n", + " \"vertebrae_T1\": 49,\n", + " \"vertebrae_C7\": 50,\n", + " \"vertebrae_C6\": 51,\n", + " \"vertebrae_C5\": 52,\n", + " \"vertebrae_C4\": 53,\n", + " \"vertebrae_C3\": 54,\n", + " \"vertebrae_C2\": 55,\n", + " \"vertebrae_C1\": 56,\n", + " \"sacrum\": 97,\n", + " \"vertebrae_S1\": 127,\n", + " },\n", + " \"Ribs\": {\n", + " \"left_rib_1\": 63,\n", + " \"left_rib_2\": 64,\n", + " \"left_rib_3\": 65,\n", + " \"left_rib_4\": 66,\n", + " \"left_rib_5\": 67,\n", + " \"left_rib_6\": 68,\n", + " \"left_rib_7\": 69,\n", + " \"left_rib_8\": 70,\n", + " \"left_rib_9\": 71,\n", + " \"left_rib_10\": 72,\n", + " \"left_rib_11\": 73,\n", + " \"left_rib_12\": 74,\n", + " \"right_rib_1\": 75,\n", + " \"right_rib_2\": 76,\n", + " \"right_rib_3\": 77,\n", + " \"right_rib_4\": 78,\n", + " \"right_rib_5\": 79,\n", + " \"right_rib_6\": 80,\n", + " \"right_rib_7\": 81,\n", + " \"right_rib_8\": 82,\n", + " \"right_rib_9\": 83,\n", + " \"right_rib_10\": 84,\n", + " \"right_rib_11\": 85,\n", + " \"right_rib_12\": 86,\n", + " \"costal_cartilages\": 114,\n", + " \"sternum\": 122,\n", + " },\n", + " \"Shoulders\": {\"left_scapula\": 89, \"right_scapula\": 90, \"left_clavicula\": 91, \"right_clavicula\": 92},\n", + " \"Hips\": {\"left_hip\": 95, \"right_hip\": 96},\n", + " \"Back_muscles\": {\n", + " \"left_gluteus_maximus\": 98,\n", + " \"right_gluteus_maximus\": 99,\n", + " \"left_gluteus_medius\": 100,\n", + " \"right_gluteus_medius\": 101,\n", + " \"left_gluteus_minimus\": 102,\n", + " \"right_gluteus_minimus\": 103,\n", + " \"left_autochthon\": 104,\n", + " \"right_autochthon\": 105,\n", + " \"left_iliopsoas\": 106,\n", + " \"right_iliopsoas\": 107,\n", + " },\n", + "}\n", + "\n", + "\n", + "def nii_to_mesh(input_nii_path, output_nii_path, output_obj_path):\n", + " \"\"\"\n", + " This function converts each organ into a separate OBJ file and generates a GLTF file\n", + " containing all organs with hierarchical structure.\n", + " It processes the input NIfTI file and groups 140 labels into 17 categories.\n", + "\n", + " Args:\n", + " input_nii_path: path to the nii file\n", + " output_nii_path: path to save the obj files\n", + " output_obj_path: path to save the gltf file\n", + " \"\"\"\n", + " if not os.path.exists(output_nii_path):\n", + " os.makedirs(output_nii_path)\n", + " pre_trans = Compose(\n", + " [\n", + " LoadImaged(keys=\"label\", ensure_channel_first=True),\n", + " BorderPadd(keys=\"label\", spatial_border=2),\n", + " SqueezeDimd(keys=\"label\", dim=0),\n", + " ]\n", + " )\n", + " orig_seg = pre_trans({\"label\": input_nii_path})[\"label\"]\n", + " all_organ = np.zeros_like(orig_seg, dtype=np.uint8)\n", + " all_label_values = {}\n", + "\n", + " save_trans = SaveImage(output_ext=\"nii.gz\", output_dtype=np.uint8)\n", + " for j, (organ_name, label_val) in enumerate(labels.items(), start=1):\n", + " single_organ = np.zeros_like(orig_seg, dtype=np.uint8)\n", + " print(f\"Assigning index {j} to label {organ_name}\")\n", + " if isinstance(label_val, dict):\n", + " for _, i in label_val.items():\n", + " all_organ[orig_seg == i] = j\n", + " single_organ[orig_seg == i] = j\n", + " else:\n", + " all_organ[orig_seg == label_val] = j\n", + " single_organ[orig_seg == label_val] = j\n", + " organ_filename = os.path.join(output_nii_path, organ_name)\n", + " save_trans(single_organ[None], meta_data=orig_seg.meta, filename=organ_filename)\n", + " convert_to_mesh(\n", + " f\"{organ_filename}.nii.gz\",\n", + " output_obj_path,\n", + " f\"{organ_name}.obj\",\n", + " label_value=j,\n", + " smoothing_factor=0.5,\n", + " reduction_ratio=0.0,\n", + " )\n", + " all_label_values[j] = organ_name\n", + "\n", + " all_organ_filename = os.path.join(output_nii_path, \"all_organs\")\n", + " save_trans(all_organ[None], meta_data=orig_seg.meta, filename=all_organ_filename)\n", + " convert_to_mesh(\n", + " f\"{all_organ_filename}.nii.gz\",\n", + " output_obj_path,\n", + " \"all_organs.gltf\",\n", + " label_value=all_label_values,\n", + " smoothing_factor=0.6,\n", + " reduction_ratio=0.0,\n", + " )\n", + " print(f\"Saved whole segmentation {all_organ_filename}\")\n", + "\n", + "\n", + "input_nii_path = f\"{bundle_root}/datasets/IntegrationTest-AbdomenCT.nii.gz\"\n", + "output_nii_path = f\"{bundle_root}/datasets/monai/nii\"\n", + "output_obj_path = f\"{bundle_root}/datasets/monai/obj\"\n", + "out = nii_to_mesh(input_nii_path, output_nii_path, output_obj_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert 3D model contain all organs to USD format\n", + "\n", + "[Universal Scene Description (OpenUSD)](https://openusd.org/release/index.html) is an extensible ecosystem of file formats, compositors, renderers, and other plugins for comprehensive 3D scene description." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "USD file successfully exported to /workspace/Data/maisi_ct_generative/datasets/monai/obj/all_organs.usd\n" + ] + } + ], + "source": [ + "obj_filename = f\"{bundle_root}/datasets/monai/obj/all_organs.gltf\"\n", + "usd_filename = f\"{bundle_root}/datasets/monai/obj/all_organs.usd\"\n", + "\n", + "convert_mesh_to_usd(obj_filename, usd_filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize one single organ mesh\n", + "\n", + "Here we randomly select one organ to visualize the mesh using `ViewInteractiveWidget`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Step 1: Read the mesh\n", + "reader = vtk.vtkOBJReader()\n", + "reader.SetFileName(f\"{bundle_root}/datasets/monai/obj/Spleen.obj\")\n", + "reader.Update()\n", + "\n", + "# Step 2: Create a mapper\n", + "mapper = vtkmodules.vtkRenderingCore.vtkPolyDataMapper()\n", + "mapper.SetInputData(reader.GetOutput())\n", + "print(f\"Number of Points: {mapper.GetInput().GetNumberOfPoints()}\")\n", + "print(f\"Number of Cells: {mapper.GetInput().GetNumberOfCells()}\")\n", + "\n", + "# Step 3: Create an actor\n", + "actor = vtkmodules.vtkRenderingCore.vtkActor()\n", + "actor.SetMapper(mapper)\n", + "\n", + "# Step 4: Create a renderer\n", + "renderer = vtk.vtkRenderer()\n", + "renderer.AddActor(actor)\n", + "render_window = vtk.vtkRenderWindow()\n", + "render_window.AddRenderer(renderer)\n", + "render_window.SetSize(800, 600)\n", + "render_window.SetOffScreenRendering(1)\n", + "\n", + "# Step 5: Create a render window interactor\n", + "render_window_interactor = vtk.vtkRenderWindowInteractor()\n", + "render_window_interactor.SetRenderWindow(render_window)\n", + "interactor_style = vtk.vtkInteractorStyleTrackballCamera()\n", + "render_window_interactor.SetInteractorStyle(interactor_style)\n", + "\n", + "# Uncomment the following line to display the interactive widget\n", + "# render_window.Render()\n", + "# interactive_widget = ViewInteractiveWidget(render_window)\n", + "# interactive_widget" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Spleen](Spleen.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization in the Omniverse\n", + "\n", + "Download the [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/) launcher to explore applications such as USD Composer for viewing and manipulating the OpenUSD output file.\n", + "\n", + "![omniverse](./omniverse.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization of All Organs\n", + "\n", + "You can view the 3D models using online viewer such as https://3dviewer.net/#\n", + "\n", + "![all organs](result.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + 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