LUND-PROBE/directoryStructure.txt at main · jamtheim/LUND-PROBE · GitHub

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## Dataset Structure

The LUND-PROBE dataset is organized with the following directory structure for each patient:

```
/Patient
├── MR_StorT2
│   ├── image.nii.gz
│   ├── mask_MRI_T2_coords_fiducials.nii.gz
│   ├── MRI_T2_DICOM_coords_fiducials.txt
│   ├── dose_interpolated.nii.gz
│   ├── mask_Bladder.nii.gz
│   ├── mask_BODY.nii.gz
│   ├── mask_CTVT_427.nii.gz
│   ├── mask_FemoralHead_R.nii.gz
│   ├── mask_FemoralHead_L.nii.gz
│   ├── mask_Genitalia.nii.gz
│   ├── mask_PenileBulb.nii.gz
│   ├── mask_PTVT_427.nii.gz
│   └── mask_Rectum.nii.gz
│
├── MR_StorT2
│   └── nnUNetOutput
│       ├── folds
│       │   ├── mask_CTVT_427_nnUNet_fold_n.nii.gz
│       │   └── mask_Rectum_nnUNet_fold_n.nii.gz
│       ├── mask_CTVT_427_nnUNet.nii.gz
│       ├── mask_CTVT_427_nnUNet_uncertaintyMap.nii.gz
│       ├── mask_Rectum_nnUNet.nii.gz
│       └── mask_Rectum_nnUNet_uncertaintyMap.nii.gz
│
├── MR_StorT2
│   └── observerData
│       ├── mask_CTVT_427_step1_obsN.nii.gz
│       ├── mask_CTVT_427_step2_obsN.nii.gz
│       ├── mask_Rectum_step1_obsN.nii.gz
│       └── mask_Rectum_step2_obsN.nii.gz
│
└── sCT
    ├── image.nii.gz
    ├── image_reg2MRI.nii.gz
    ├── dose_original.nii.gz
    ├── dose_interpolated.nii.gz
    ├── mask_Bladder.nii.gz
    ├── mask_BODY.nii.gz
    ├── mask_CTVT_427.nii.gz
    ├── mask_FemoralHead_R.nii.gz
    ├── mask_FemoralHead_L.nii.gz
    ├── mask_Genitalia.nii.gz
    ├── mask_PenileBulb.nii.gz
    ├── mask_PTVT_427.nii.gz
    └── mask_Rectum.nii.gz
```

### Directory Descriptions:

- **MR_StorT2/**: Contains MRI T2-weighted images and related segmentation masks
  - `image.nii.gz`: Primary MRI T2-weighted image
  - `mask_*`: Various anatomical structure segmentation masks
  - `dose_interpolated.nii.gz`: Dose distribution interpolated to MRI geometry
  - `MRI_T2_DICOM_coords_fiducials.txt`: Fiducial marker coordinates in DICOM format

- **MR_StorT2/nnUNetOutput/**: Deep learning generated segmentations and uncertainty maps
  - `mask_*_nnUNet.nii.gz`: Deep learning generated segmentations
  - `mask_*_nnUNet_uncertaintyMap.nii.gz`: Uncertainty maps for Deep learning segmentations
  - `folds/`: Cross-validation fold results

- **MR_StorT2/observerData/**: Manual segmentations from multiple radiation oncologists
  - `mask_*_step1_obsN.nii.gz`: Adjusted observer segmentations from step 1 (N = observer ID)
  - `mask_*_step2_obsN.nii.gz`: Adjusted observer segmentations from step 2

- **sCT/**: Synthetic CT images and related data
  - `image.nii.gz`: Synthetic CT image
  - `image_reg2MRI.nii.gz`: Synthetic CT registered to MRI geometry
  - `dose_original.nii.gz`: Original dose distribution
  - `dose_interpolated.nii.gz`: Dose distribution interpolated to sCT geometry
  - `mask_*`: Segmentation masks corresponding to sCT geometry