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3D Slicer医学图像全自动AI分割组合拳-MONAIAuto3DSeg扩展

news 2024/11/16 2:14:28
1 官网下载最新3D Slicer image computing platform | 3D Slicer 版本5.7
2 安装torch依赖包：
2.1 进入安装目录C:\Users\wangzhenlin\AppData\Local\slicer.org\Slicer 5.7.0-2024-09-21\bin，安装下载好的whl文件，slicer对应的是python3.9版本。
2.2 参考python playsound插件下载 python插件库_kcoufee的技术博客_51CTO博客
在自己conda环境下安装好，之后copy到slicer的文件夹内：
slicer的 Lib/site-packages路径：C:\Users\wangzhenlin\AppData\Local\slicer.org\Slicer 5.7.0-2024-09-21\lib\Python\Lib\site-packages
conda的 Lib/site-packages路径：D:\ProgramData\Anaconda3\envs\slicer39\Lib\site-packages
3 最后slicer自动安装对应的包
4模型下载地址：C:\Users\wangzhenlin\.MONAIAuto3DSeg\models\abdominal-organs-3mm-v2.0.0
log记录：
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Collecting monai[fire,itk,nibabel,psutil,pynrrd,pyyaml,skimage,tensorboard,tqdm]>=1.3Obtaining dependency information for monai[fire,itk,nibabel,psutil,pynrrd,pyyaml,skimage,tensorboard,tqdm]>=1.3 from https://files.pythonhosted.org/packages/bc/74/42d4ab8ee0a32c23ac7d38912c0d9d7c30de6e36b601e68bd2538452309b/monai-1.3.2-py3-none-any.whl.metadataDownloading monai-1.3.2-py3-none-any.whl.metadata (10 kB)
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Building wheels for collected packages: fireBuilding wheel for fire (pyproject.toml): startedBuilding wheel for fire (pyproject.toml): finished with status 'done'Created wheel for fire: filename=fire-0.6.0-py2.py3-none-any.whl size=117044 sha256=56c65e2b67ee319bb7643cfab4ee2b3313f097656f7ca21125f2c6b87247ce73Stored in directory: c:\users\wangzhenlin\appdata\local\pip\cache\wheels\ec\ce\ba\9d5764d2266c500c18776c7d8f1e3c023075994cbc6dea47db
Successfully built fire
Installing collected packages: mpmath, zipp, tifffile, termcolor, tensorboard-data-server, sympy, pyyaml, psutil, protobuf, nptyping, nibabel, networkx, MarkupSafe, lazy-loader, itk-core, imageio, grpcio, fsspec, filelock, colorama, absl-py, werkzeug, tqdm, scikit-image, pynrrd, jinja2, itk-numerics, itk-io, importlib-metadata, fire, markdown, itk-filtering, tensorboard, monai, itk-segmentation, itk-registration, itk
Successfully installed MarkupSafe-2.1.5 absl-py-2.1.0 colorama-0.4.6 filelock-3.16.1 fire-0.6.0 fsspec-2024.9.0 grpcio-1.66.1 imageio-2.35.1 importlib-metadata-8.5.0 itk-5.4.0 itk-core-5.4.0 itk-filtering-5.4.0 itk-io-5.4.0 itk-numerics-5.4.0 itk-registration-5.4.0 itk-segmentation-5.4.0 jinja2-3.1.4 lazy-loader-0.4 markdown-3.7 monai-1.3.2 mpmath-1.3.0 networkx-3.2.1 nibabel-5.2.1 nptyping-2.5.0 protobuf-5.28.2 psutil-6.0.0 pynrrd-1.0.0 pyyaml-6.0.2 scikit-image-0.24.0 sympy-1.13.3 tensorboard-2.17.1 tensorboard-data-server-0.7.2 termcolor-2.4.0 tifffile-2024.8.30 tqdm-4.66.5 werkzeug-3.0.4 zipp-3.20.2Initializing PyTorch...
Initializing MONAI...
Dependencies are set up successfully.
Downloading model 'abdominal-organs-3mm-v2.0.0' from https://github.com/lassoan/SlicerMONAIAuto3DSeg/releases/download/Models/abdominal-organs-3mm-v2.0.0.zip...
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Download finished. Extracting to C:\Users\wangzhenlin\.MONAIAuto3DSeg\models\abdominal-organs-3mm-v2.0.0...
Cleaning up temporary model download folder...
Processing started
Writing input file to C:/Users/wangzhenlin/AppData/Local/Temp/Slicer/__SlicerTemp__2024-09-24_17+56+23.048/input-volume0.nrrd
Creating segmentations with MONAIAuto3DSeg AI...
Auto3DSeg command: ['C:/Users/wangzhenlin/AppData/Local/slicer.org/Slicer 5.7.0-2024-09-21/bin/../bin\\PythonSlicer.EXE', 'C:/Users/wangzhenlin/AppData/Local/slicer.org/Slicer 5.7.0-2024-09-21/slicer.org/Extensions-33025/MONAIAuto3DSeg/lib/Slicer-5.7/qt-scripted-modules\\Scripts\\auto3dseg_segresnet_inference.py', '--model-file', 'C:\\Users\\wangzhenlin\\.MONAIAuto3DSeg\\models\\abdominal-organs-3mm-v2.0.0\\model.pt', '--image-file', 'C:/Users/wangzhenlin/AppData/Local/Temp/Slicer/__SlicerTemp__2024-09-24_17+56+23.048/input-volume0.nrrd', '--result-file', 'C:/Users/wangzhenlin/AppData/Local/Temp/Slicer/__SlicerTemp__2024-09-24_17+56+23.048/output-segmentation.nrrd']
`apex.normalization.InstanceNorm3dNVFuser` is not installed properly, use nn.InstanceNorm3d instead.
Model epoch 294 metric 0.9070999026298523
Using crop_foreground
Using resample with  resample_resolution [3.0, 3.0, 3.0]
Running Inference ...preds inverted torch.Size([512, 512, 88])
Computation time log:Loading volumes: 2.19 seconds
Importing segmentation results...
Cleaning up temporary folder.
Processing was completed in 22.38 seconds.Processing finished.
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