Update Install-nnUnet.md

Note/docs/Install-nnUnet.md

@@ -1 +1,65 @@
 # Install nnUnet
+* out-of-the-box segmentation algorithm
+`pip install nnunetv2`
+* to modify code, open a terminal, select a directory:
+```
+git clone https://github.com/MIC-DKFZ/nnUNet.git
+cd nnUNet
+pip install -e .
+```
+1. Create 3 folders in folder `nnUNet`, `nnUNet_raw`, `nnUNet_preprocessed`, `nnUNet_results`
+2. Set environment variables
+    - Linux, temporary in terminal (permanent in home folder `.bashrc`):
+    - Windows, temporary in Command prompt (permanent in Edit Environment Variable GUI):
+    - in code: `/home/jing/pycode/nnUNet/nnunetv2/paths.py`
+
+```
+# in Linux
+export nnUNet_raw='/home/jing/pycode/nnUNet/nnUNet_raw'
+export nnUNet_preprocessed='/home/jing/pycode/nnUNet/nnUNet_preprocessed'
+export nnUNet_results='/home/jing/pycode/nnUNet/nnUNet_results'
+# in Windows
+set nnUNet_raw=C:/Users/jing/pycode/nnUNet_raw
+set nnUNet_preprocessed=C:/Users/jing/pycode/nnUNet_preprocessed
+set nnUNet_results=C:/Users/jing/pycode/nnUNet_results
+# in code, added by user
+os.environ['nnUNet_raw'] = r'/home/jing/pycode/nnUNet/nnUNet_raw'
+os.environ['nnUNet_preprocessed'] = r'/home/jing/pycode/nnUNet/nnUNet_preprocessed'
+os.environ['nnUNet_results'] = r'/home/jing/pycode/nnUNet/nnUNet_results'
+```
+3. Dataset conversion    
+   in file `/home/jing/pycode/nnUNet/pyproject.toml`, a sentence like
+```
+nnUNetv2_convert_MSD_dataset = "nnunetv2.dataset_conversion.convert_MSD_dataset:entry_point"
+``` 
+shows the path, along the path find out file `convert_MSD_dataset.py`, in the last line, change the parameter, then run it.    
+```
+convert_msd_dataset('/home/jing/pycode/nnUNet/nnUNet_raw/Task05_Prostate', overwrite_target_id=1101)
+```
+4. Experiment planning and preprocessing
+     - in terminal `nnUNetv2_plan_and_preprocess -d DATASET_ID --verify_dataset_integrity # DATASET_ID is 1101`
+     - in code, search `nnUNetv2_plan_and_preprocess` in the file `pyproject.toml`, you will find:
+```
+nnUNetv2_plan_and_preprocess = "nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:plan_and_preprocess_entry"
+```
+then go to this file  `plan_and_preprocess_entry.py`, then run it after adding a default parameter in function `plan_and_preprocess_entry`:
+```
+parser.add_argument('-d', nargs='+', type=int,
+help="[REQUIRED] List of dataset IDs. Example: 2 4 5. This will run fingerprint extraction, experiment "
+"planning and preprocessing for these datasets. Can of course also be just one dataset", default=[1101])
+```
+5. Model training
+   - in terminal `nnUNetv2_train DATASET_NAME_OR_ID UNET_CONFIGURATION FOLD [additional options, see -h]`
+   - in code, search `nnUNetv2_train` in the file `pyproject.toml`, you will find:
+```
+nnUNetv2_train = "nnunetv2.run.run_training:run_training_entry"
+```
+then go to this file  `run_training_entry.py`, then run it after adding a default parameter in function `run_training_entry`:
+```
+parser.add_argument('dataset_name_or_id', type=str, required=False, help="Dataset name or ID to train with", default='1101')
+parser.add_argument('configuration', type=str, required=False, help="Configuration that should be trained")
+parser.add_argument('fold', type=str, default='1', required=False, help='Fold of the 5-fold cross-validation. Should be an int between 0 and 4.')
+```
+6. Model predicting (similar as above)
+
+✴️ reference: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/how_to_use_nnunet.md