| Name | CardiacModelGenerator JSON |
| Version |
0.1.9
JSON |
| download |
| home_page | None |
| Summary | Generate 3D model from clinical cardiac imaging data |
| upload_time | 2024-12-08 06:54:15 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2024 vjaniuw Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
3d
vtk
cardiac
medical imaging
modeling
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# CardiacModelGenerator
## Overview
CardiacModelGenerator.py is a Python-based application designed for viewing slice overlays, converting pixels to universal coordinates, generating point clouds, and generating/enhancing tetrahedral meshes. Specifically, this is for cardiac models and uses MRI DICOM images and nifti masks.
### How to Use
1. First install via
```bash
pip install CardiacModelGenerator
```
2. Then run the following in python either in an IDE or via terminal:
```bash
from CardiacModelGenerator import CardiacModelGenerator
CardiacModelGenerator.main()
```
__If you are using macOS or Apple Silicon__:
__Use pythonw. If not wx will not work__
An example is below:
If successfully run, the following should appear:
3. Dowload the files in the example files. There should be a patient27 folder which has dicoms and a nii file.
4. Click Load ImageView1 and navigate/click to the patient27 folder:
5. Do the same with the LoadSegs; however, navigate and click the nii file:
6. After this you can use the tool. First you can click View Seg1 and scroll through the overlays.
7. Now, you can generate the point cloud, tetra mesh, clean the mesh, and get the quality metric. Ensure you do this
in this order. To access these functions, go to the mesh processing at the top.
8. For the point cloud, you have to give parameters. Examples of parameters is below:
9. After you make a mesh by clicking the make tetra mesh option. You can clean the mesh. Example parameters are below:
10. Finally, you can click the extract mesh quality.
## Features
Image/Mask Viewer: Allows for a user to scroll through overlays of a mask and image Point Clouds: Can generate point cloud based on user inputs Universal Coordinates: Convers Mask/Image data to universal coordinates based on Dicom metadata Mesh: Allows for tetrahedral meshes from user inputs
### Requirements
__<u>All the requirements should be installed when you pip install the package. <\u>__
If there are issues:
wx numpy pydicom nibabel cv2 (OpenCV) random matplotlib pyvista Install dependencies using:
pip install wxpython numpy pydicom nibabel opencv-python matplotlib pyvista How to Use
### Input Data
Input Data: Prepare images in a folder (should be dicoms). Have masks as nifti. Examples of data are given in the github.
### GUI
Interactive GUI: The script uses wx for GUI, allowing you to interactively select data and configure settings. Visualize Point Clouds: Choose from multiple colormaps and adjust parameters like point_size and tol.
Select Dicom Image Folder
User selects mask for that folder
User clicks view segmentation
User selects generate Point Cloud
User selects generate mesh
User selects fix mesh
User can look at quality by clicking mesh quality
Developed by vinayjani. Contributions and suggestions are welcome!
License
This project is licensed under the MIT License. See LICENSE for details.
Raw data
{
"_id": null,
"home_page": null,
"name": "CardiacModelGenerator",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "3D, VTK, cardiac, medical imaging, modeling",
"author": null,
"author_email": "Vinay Jani <vjani@uw.edu>",
"download_url": "https://files.pythonhosted.org/packages/c8/6b/a23a95ff963e84f8773f6aa22d1ac44afa1dcc93c8368be99a8023d7057a/cardiacmodelgenerator-0.1.9.tar.gz",
"platform": null,
"description": "# CardiacModelGenerator\n\n\n## Overview\n\nCardiacModelGenerator.py is a Python-based application designed for viewing slice overlays, converting pixels to universal coordinates, generating point clouds, and generating/enhancing tetrahedral meshes. Specifically, this is for cardiac models and uses MRI DICOM images and nifti masks.\n\n\n### How to Use\n\n1. First install via \n\n```bash\npip install CardiacModelGenerator\n```\n2. Then run the following in python either in an IDE or via terminal: \n\n```bash \nfrom CardiacModelGenerator import CardiacModelGenerator \nCardiacModelGenerator.main()\n```\n\n__If you are using macOS or Apple Silicon__: \n\n__Use pythonw. If not wx will not work__\n\nAn example is below: \n\n\n\nIf successfully run, the following should appear: \n\n\n\n3. Dowload the files in the example files. There should be a patient27 folder which has dicoms and a nii file. \n\n4. Click Load ImageView1 and navigate/click to the patient27 folder: \n\n\n\n5. Do the same with the LoadSegs; however, navigate and click the nii file: \n\n\n\n6. After this you can use the tool. First you can click View Seg1 and scroll through the overlays. \n\n\n7. Now, you can generate the point cloud, tetra mesh, clean the mesh, and get the quality metric. Ensure you do this \nin this order. To access these functions, go to the mesh processing at the top. \n\n\n8. For the point cloud, you have to give parameters. Examples of parameters is below: \n\n\n9. After you make a mesh by clicking the make tetra mesh option. You can clean the mesh. Example parameters are below: \n\n\n10. Finally, you can click the extract mesh quality. \n\n\n\n## Features\n\nImage/Mask Viewer: Allows for a user to scroll through overlays of a mask and image Point Clouds: Can generate point cloud based on user inputs Universal Coordinates: Convers Mask/Image data to universal coordinates based on Dicom metadata Mesh: Allows for tetrahedral meshes from user inputs\n\n### Requirements\n\n__<u>All the requirements should be installed when you pip install the package. <\\u>__\n\nIf there are issues: \n\nwx numpy pydicom nibabel cv2 (OpenCV) random matplotlib pyvista Install dependencies using:\n\npip install wxpython numpy pydicom nibabel opencv-python matplotlib pyvista How to Use\n\n### Input Data \nInput Data: Prepare images in a folder (should be dicoms). Have masks as nifti. Examples of data are given in the github. \n\n\n### GUI\nInteractive GUI: The script uses wx for GUI, allowing you to interactively select data and configure settings. Visualize Point Clouds: Choose from multiple colormaps and adjust parameters like point_size and tol. \nSelect Dicom Image Folder\nUser selects mask for that folder\nUser clicks view segmentation\nUser selects generate Point Cloud\nUser selects generate mesh\nUser selects fix mesh\nUser can look at quality by clicking mesh quality\n\n\n\nDeveloped by vinayjani. Contributions and suggestions are welcome!\n\nLicense\n\nThis project is licensed under the MIT License. See LICENSE for details.",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2024 vjaniuw Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.",
"summary": "Generate 3D model from clinical cardiac imaging data",
"version": "0.1.9",
"project_urls": {
"Bug Tracker": "https://github.com/vjaniuw/CardiacModelGenerator/issues",
"Documentation": "https://github.com/vjaniuw/CardiacModelGenerator/wiki",
"Homepage": "https://github.com/vjaniuw/CardiacModelGenerator",
"Repository": "https://github.com/vjaniuw/CardiacModelGenerator"
},
"split_keywords": [
"3d",
" vtk",
" cardiac",
" medical imaging",
" modeling"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "a338ce606fb5382ba48886b3b7acaa7b0381dc26c2a279cc90f2be95b5c1ab94",
"md5": "8d5d7b139bb3f95c52486d283d421ee8",
"sha256": "1ee57a4d635a7236fdc8c0c9e22bd5c8c22f2fd18298c0f6665ea62f96da7ee5"
},
"downloads": -1,
"filename": "cardiacmodelgenerator-0.1.9-py3-none-any.whl",
"has_sig": false,
"md5_digest": "8d5d7b139bb3f95c52486d283d421ee8",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 5069711,
"upload_time": "2024-12-08T06:54:12",
"upload_time_iso_8601": "2024-12-08T06:54:12.586485Z",
"url": "https://files.pythonhosted.org/packages/a3/38/ce606fb5382ba48886b3b7acaa7b0381dc26c2a279cc90f2be95b5c1ab94/cardiacmodelgenerator-0.1.9-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c86ba23a95ff963e84f8773f6aa22d1ac44afa1dcc93c8368be99a8023d7057a",
"md5": "1bf8210e300b132b5bfe28953c2154ec",
"sha256": "d8102b3798157e86d7e2f0e74a6bdcce90466e0321df56f9459867636f3b1b41"
},
"downloads": -1,
"filename": "cardiacmodelgenerator-0.1.9.tar.gz",
"has_sig": false,
"md5_digest": "1bf8210e300b132b5bfe28953c2154ec",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 31260482,
"upload_time": "2024-12-08T06:54:15",
"upload_time_iso_8601": "2024-12-08T06:54:15.617682Z",
"url": "https://files.pythonhosted.org/packages/c8/6b/a23a95ff963e84f8773f6aa22d1ac44afa1dcc93c8368be99a8023d7057a/cardiacmodelgenerator-0.1.9.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-08 06:54:15",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "vjaniuw",
"github_project": "CardiacModelGenerator",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "cardiacmodelgenerator"
}
