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

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]>=1.3
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Initializing 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.