rsummers11 / ascites

Ascites

Ascites Segmentation with nnUNet

Ascites Dataset (TCGA-OV-AS)

This dataset is derived from TCGA-OV Dataset. Download the data from TCIA with Descriptive Directory Name download option.

Converting Images

Convert the DICOMs to NIFTI format using dcm2niix and GNU parallel.

1. Create the directory structure required for each NIFTI file:

   1. find TCGA-OV -type d -exec mkdir -p -- /tmp/{} \;
   2. mv /tmp/TCGA-OV ./TCGA-OV-NIFTI

2. Convert DICOMs to NIFTI

   1. parallel --jobs $n < jobs.txt where $n is number of parallel jobs.

Ascites Labels

285 images that are free of corruption have been hand-picked for use, images mostly consist of ABDOMEN-PELVIS scans. Labels can be downloaded here.

Clinical Information

Patient clinical data can be downloaded from TCIA: TCGA-OV Clinical Data.zip

Method 1: Run Inference using nnunet_predict.py

1. Install the latest version of nnUNet and PyTorch.

user@machine:~/ascites_segmentation$ pip install torch torchvision torchaudio nnunet matplotlib

2. Download model and weights:

• ascites_model.tar.gz
  Size: 1.1G
  MD5: d39aa7f8d29bddd4edf9e4b24a8f55a1

3. Run inference with command:

user@machine:~/ascites_segmentation$ python nnunet_predict.py -i file_list.txt -t TMP_DIR -o OUTPUT_FOLDER -m /path/to/nnunet/model_weights

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 and PyTorch.

user@machine:~/ascites_segmentation$ pip install torch torchvision torchaudio nnunet matplotlib

2. Download model and weights:

• ascites_model.tar.gz
  Size: 1.1G
  MD5: d39aa7f8d29bddd4edf9e4b24a8f55a1

3. Place checkpoints in directory tree:

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

4. Setup environment variables so that nnU-Net knows where to find trained models:

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"

5. Run inference with command:

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:

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). 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:

user@machine:~/ascites_segmentation$ sudo docker build -t nnunet_docker .

2. Run docker image on test volumes:

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