This is a tutorial adapted from Spleen 3D segmentation with MONAI.
This tutorial shows how to integrate MONAI into an existing PyTorch medical DL program.
And easily use below features:
- Transforms for dictionary format data.
- Load Nifti image with metadata.
- Add channel dim to the data if no channel dimension.
- Scale medical image intensity with expected range.
- Crop out a batch of balanced images based on positive / negative label ratio.
- Cache IO and transforms to accelerate training and validation.
- 3D UNet model, Dice loss function, Mean Dice metric for 3D segmentation task.
- Sliding window inference method.
- Deterministic training for reproducibility.
- The Spleen dataset can be downloaded from http://medicaldecathlon.com/.
Target: Spleen Modality: CT Size: 61 3D volumes (41 Training + 20 Testing) Source: Memorial Sloan Kettering Cancer Center Challenge: Large ranging foreground size
Start the tutorial spleen_segmentation_3d.ipynb with the mphy0043 conda env.
