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---
license: mit
---
# Ascites Segmentation with nnUNet
## Method 1: Run Inference using `nnunet_predict.py`
1. Install the latest version of [nnUNet](https://github.com/MIC-DKFZ/nnUNet#installation) and [PyTorch](https://pytorch.org/get-started/locally/).
```shell
user@machine:~/ascites_segmentation$ pip install torch torchvision torchaudio nnunet matplotlib
```
2. Run inference with command:
```shell
user@machine:~/ascites_segmentation$ python nnunet_predict.py -i file_list.txt -t TMP_DIR -o OUTPUT_FOLDER -m /path/to/nnunet/model_weights
```
```shell
usage: tmp.py [-h] [-i INPUT_LIST] -t TMP_FOLDER -o OUTPUT_FOLDER -m MODEL [-v]
Inference using nnU-Net predict_from_folder Python API
optional arguments:
-h, --help show this help message and exit
-i INPUT_LIST, --input_list INPUT_LIST
Input image file_list.txt
-t TMP_FOLDER, --tmp_folder TMP_FOLDER
Temporary folder
-o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER
Output Segmentation folder
-m MODEL, --model MODEL
Trained Model
-v, --verbose Verbose Output
```
N.B.
- `model_weights` folder should contain `fold0`, `fold1`, etc...
- WARNING: the program will try to create file links first, but will fallback to filecopy if fails
## Method 2: Run Inference using `nnUNet_predict` from shell
1. Install the latest version of [nnUNet](https://github.com/MIC-DKFZ/nnUNet#installation) and [PyTorch](https://pytorch.org/get-started/locally/).
```shell
user@machine:~/ascites_segmentation$ pip install torch torchvision torchaudio nnunet matplotlib
```
2. Place checkpoints in directory tree:
```shell
user@machine:~/ascites_segmentation$ tree .
.
├── nnUNet_preprocessed
├── nnUNet_raw_data_base
└── nnUNet_trained_models
└── nnUNet
└── 3d_fullres
└── Task505_TCGA-OV
└── nnUNetTrainerV2__nnUNetPlansv2.1
├── fold_0
│ ├── debug.json
│ ├── model_final_checkpoint.model
│ ├── model_final_checkpoint.model.pkl
│ └── progress.png
├── fold_1
│ ├── debug.json
│ ├── model_final_checkpoint.model
│ ├── model_final_checkpoint.model.pkl
│ └── progress.png
├── fold_2
│ ├── model_final_checkpoint.model
│ ├── model_final_checkpoint.model.pkl
│ └── progress.png
├── fold_3
│ ├── model_final_checkpoint.model
│ ├── model_final_checkpoint.model.pkl
│ └── progress.png
├── fold_4
│ ├── model_final_checkpoint.model
│ ├── model_final_checkpoint.model.pkl
│ └── progress.png
└── plans.pkl
```
3. Setup environment variables so that nnU-Net knows where to find trained models:
```shell
user@machine:~/ascites_segmentation$ export nnUNet_raw_data_base="/absolute/path/to/nnUNet_raw_data_base"
user@machine:~/ascites_segmentation$ export nnUNet_preprocessed="/absolute/path/to/nnUNet_preprocessed"
user@machine:~/ascites_segmentation$ export RESULTS_FOLDER="/absolute/path/to/nnUNet_trained_models"
```
4. Run inference with command:
```shell
user@machine:~/ascites_segmentation$ nnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t 505 -m 3d_fullres -f N --save_npz
```
where:
- `-i`: input folder of `.nii.gz` scans to predict. NB, filename needs to end with `_0000.nii.gz` to tell nnU-Net only one kind of modality
- `-o`: output folder to store predicted segmentations, automatically created if not exist
- `-t 505`: (do not change) Ascites pretrained model name
- `-m 3d_fullres` (do not change) Ascites pretrained model name
- `N`: Ascites pretrained model fold, can be `[0, 1, 2, 3, 4]`
- `--save_npz`: save softmax scores, required for ensembling multiple folds
### Optional [Additional] Inference Steps
a. use `nnUNet_find_best_configuration` to automatically get the inference commands needed to run the trained model on data.
b. ensemble predictions using `nnUNet_ensemble` by running:
```shell
user@machine:~/ascites_segmentation$ nnUNet_ensemble -f FOLDER1 FOLDER2 ... -o OUTPUT_FOLDER -pp POSTPROCESSING_FILE
```
where `FOLDER1` and `FOLDER2` are predicted outputs by nnUNet (requires `--save_npz` when running `nnUNet_predict`).
## Method 3: Docker Inference
Requires `nvidia-docker` to be installed on the system ([Installation Guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)). This `nnunet_docker` predicts ascites with all 5 trained folds and ensembles output to a single prediction.
1. Build the `nnunet_docker` image from `Dockerfile`:
```shell
user@machine:~/ascites_segmentation$ sudo docker build -t nnunet_docker .
```
2. Run docker image on test volumes:
```shell
user@machine:~/ascites_segmentation$ sudo docker run \
--gpus 0 \
--volume /absolute/path/to/INPUT_FOLDER:/tmp/INPUT_FOLDER \
--volume /absolute/path/to/OUTPUT_FOLDER:/tmp/OUTPUT_FOLDER \
nnunet_docker /bin/sh inference.sh
```
- `--gpus` parameter:
- `0, 1, 2, ..., n` for integer number of GPUs
- `all` for all available GPUs on the system
- `'"device=2,3"'` for specific GPU with ID
- `--volume` parameter
- `/absolute/path/to/INPUT_FOLDER` and `/absolute/path/to/OUTPUT_FOLDER` folders on the host system needs to be specified
- `INPUT_FOLDER` contains all `.nii.gz` volumes to be predicted
- predicted results will be written to `OUTPUT_FOLDER`
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