@@ -9,7 +9,6 @@ Please read the information from the homepage carefully and follow the rules and
0. Follow the installation instructions of nnDetection and create the data directories for the intended tasks, e.g. `Task003_Liver`.
1. Follow the instructions and usage policies to download the data and place the images, labels and dataset.json files inside the raw folder of the respective tasks, e.g. imagesTr -> `Task003_Liver / raw / imagesTr`, labelsTr -> `Task003_Liver / raw / labelsTr` and dataset.json -> `Task003_Liver / raw / dataset.json`
2. Run `python prepare.py [tasks]` in `projects / Task001_Decathlion / scripts` of the nnDetection repository, e.g. to prepare all tasks: `python prepare.py Task003_Liver Task007_Pancreas Task008_HepaticVessel Task010_Colon`
3. Run `nndet_seg2det [tasks]` to convert the semantic segmentation labels to instance segmentations, e.g. to convert all tasks `nndet_seg2det 003 007 008 010`
4. Run ... to download and replace the manually corrected labels. # TODO
3. Download labels from [here](https://zenodo.org/record/4876497#.YLSudzYzYeY) and replace `labelsTr` / `labelsTs` in the splitted folder with the downloaded ones.
The data is now converted to the correct format and the instructions from the nnDetection README can be used to train the networks.
@@ -9,7 +9,7 @@ Please read the information from the homepage carefully and follow the rules and
0. Follow the installation instructions of nnDetection and create a data directory name `Task011_Kits`.
1. Follow the instructions and usage policies to download the data and place all the folders which contain the data and labels for each case into `Task011_Kits / raw`
2. Run `python prepare.py` in `projects / Task011_Kits / scripts` of the nnDetection repository.
3. Run `nndet_seg2det 011` to convert the semantic segmentation labels to instance segmentations.
4.Run ... to download and replace the manually corrected labels. # TODO
@@ -10,6 +10,7 @@ Please read the information from the homepage carefully and follow the rules and
1. Follow the installation instructions of nnDetection and create a data directory name `Task012_LIDC`.
2. Place the `data_nrrd` folder and `characteristics.csv` into `Task012_LIDC / raw`
3. Run `python prepare_mic.py` in `projects / Task012_LIDC / scripts` of the nnDetection repository.
4. Copy the `splits_final.pkl` from `projects / Task012_LIDC` into the preprocessed folder of the data (if the preprocessing wasn't run until now, it is nesseary to manually create the folder)
The data is now converted to the correct format and the instructions from the nnDetection README can be used to train the networks.
