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feat: update to nnunetV2 #145

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feat: update to nnunetV2 #145

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b388e8e
fix: removed repeated documentation on nnUnet page
JoshuaSiraj Nov 27, 2024
90bd7d8
fix: removed repeated documentation on nnunet page
JoshuaSiraj Nov 27, 2024
172b595
feat: updated to generate_dataset_json func from nnunetv2
JoshuaSiraj Nov 27, 2024
bf69ab2
feat: changed to nnunetv2 dataset naming convention(TaskXXX_NAME - > …
JoshuaSiraj Nov 27, 2024
bca5c54
feat: updated to nnunet folder convention(https://github.com/MIC-DKFZ…
JoshuaSiraj Nov 27, 2024
908e8e2
feat: changed to nnunetv2 file naming(0_RADCURE-0005_0000.nii.gz -> …
JoshuaSiraj Nov 27, 2024
7d76c05
style: added comments and updated markdown_report_images function for…
JoshuaSiraj Nov 28, 2024
e9cf057
fix: using nnunetv2 functions in train.sh and updated string splicing…
JoshuaSiraj Nov 28, 2024
dfa1ce1
docs: updated nnunet page to reflect v2 changes
JoshuaSiraj Nov 28, 2024
3ed91bc
resolved pr comments
JoshuaSiraj Nov 28, 2024
1a0552d
fix: small bugs
JoshuaSiraj Nov 28, 2024
615a232
fix: update to file name and taking care of patients that have no ROI…
JoshuaSiraj Nov 29, 2024
d3e1a11
docs: update to nnunet stuff on main page and minor edit on nnunet page
JoshuaSiraj Dec 2, 2024
caf5da5
refactor: small type update + gitignore updated to ignore test outs
JoshuaSiraj Dec 16, 2024
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151 changes: 18 additions & 133 deletions docs/nnUNet.md
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Original file line number Diff line number Diff line change
Expand Up @@ -19,122 +19,7 @@ autopipeline\
Modalities can also be set to `--modalities MR,RTSTRUCT`

AutoPipeline offers many more options and features for you to customize your outputs: <<https://github.com/bhklab/med-imagetools/tree/main/README.md>
>.

## nnUNet Preprocess and Train

### One-Step Preprocess and Train

Med-ImageTools generates a file in your output folder called `nnunet_preprocess_and_train.sh` that combines all the commands needed for preprocessing and training your nnUNet model. Run that shell script to get a fully trained nnUNet model.

Alternatively, you can go through each step individually as follows below:

### nnUNet Preprocessing

Follow the instructions for setting up your paths for nnUNet: <https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/setting_up_paths.md>

Med-ImageTools generates the dataset.json that nnUNet requires in the output directory that you specify.

The generated output directory structure will look something like:

```sh
OUTPUT_DIRECTORY
├── nnUNet_preprocessed
├── nnUNet_raw_data_base
│ └── nnUNet_raw_data
│ └── Task500_HNSCC
│ ├── nnunet_preprocess_and_train.sh
│ └── ...
└── nnUNet_trained_models
```

nnUNet requires that environment variables be set before any commands are executed. To temporarily set them, run the following:

```sh
export nnUNet_raw_data_base="/OUTPUT_DIRECTORY/nnUNet_raw_data_base"
export nnUNet_preprocessed="/OUTPUT_DIRECTORY/nnUNet_preprocessed"
export RESULTS_FOLDER="/OUTPUT_DIRECTORY/nnUNet_trained_models"
```

To permanently set these environment variables, make sure that in your `~/.bashrc` file, these environment variables are set for nnUNet. The `nnUNet_preprocessed` and `nnUNet_trained_models` folders are generated as empty folders for you by Med-ImageTools. `nnUNet_raw_data_base` is populated with the required raw data files. Add this to the file:

```sh
export nnUNet_raw_data_base="/OUTPUT_DIRECTORY/nnUNet_raw_data_base"
export nnUNet_preprocessed="/OUTPUT_DIRECTORY/nnUNet_preprocessed"
export RESULTS_FOLDER="/OUTPUT_DIRECTORY/nnUNet_trained_models"
```

Then, execute the command:

```sh
source ~/.bashrc
```

Too allow nnUNet to preprocess your data for training, run the following command. Set XXX to the ID that you want to preprocess. This is your task ID. For example, for Task500_HNSCC, the task ID is 500. Task IDs must be between 500 and 999, so Med-ImageTools can run 500 instances with the `--nnunet` flag in a single output folder.

```sh
nnUNet_plan_and_preprocess -t XXX --verify_dataset_integrity
```

### nnUNet Training

Once nnUNet has finished preprocessing, you may begin training your nnUNet model. To train your model, run the following command. Learn more about nnUNet's options here: <https://github.com/MIC-DKFZ/nnUNet#model-training>

```sh
nnUNet_train CONFIGURATION TRAINER_CLASS_NAME TASK_NAME_OR_ID FOLD
```

## nnUNet Inference

For inference data, nnUNet requires data to be in a different output format. To run AutoPipeline for nnUNet inference, run the following command:

```sh
autopipeline\
[INPUT_DIRECTORY] \
[OUTPUT_DIRECTORY] \
--modalities CT \
--nnunet_inference \
--dataset_json_path [DATASET_JSON_PATH]
```
To execute this command AutoPipeline needs a json file with the image modality definitions.

Modalities can also be set to `--modalities MR`.

The directory structue will look like:

```sh
OUTPUT_DIRECTORY
├── 0_subject1_0000.nii.gz
└── ...
```

To run inference, run the command:

```sh
nnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m CONFIGURATION
```

In this case, the `INPUT_FOLDER` of nnUNet is the `OUTPUT_DIRECTORY` of Med-ImageTools.# Preparing Data for nnUNet

nnUNet repo can be found at: <https://github.com/MIC-DKFZ/nnUNet>

## Processing DICOM Data with Med-ImageTools

Ensure that you have followed the steps in <https://github.com/bhklab/med-imagetools#installing-med-imagetools> before proceeding.

To convert your data from DICOM to NIfTI for training an nnUNet auto-segmentation model, run the following command:

```sh
autopipeline\
[INPUT_DIRECTORY] \
[OUTPUT_DIRECTORY] \
--modalities CT,RTSTRUCT \
--nnunet
```

Modalities can also be set to `--modalities MR,RTSTRUCT`

AutoPipeline offers many more options and features for you to customize your outputs: <https://github.com/bhklab/med-imagetools/imgtools/README.md>.

## nnUNet Preprocess and Train

Expand All @@ -155,28 +40,28 @@ The generated output directory structure will look something like:
```sh
OUTPUT_DIRECTORY
├── nnUNet_preprocessed
├── nnUNet_raw_data_base
│ └── nnUNet_raw_data
│ └── Task500_HNSCC
│ ├── nnunet_preprocess_and_train.sh
│ └── ...
└── nnUNet_trained_models
├── nnUNet_raw
│   └── Dataset001_HNSCC
│   ├── nnunet_preprocess_and_train.sh
│   └── ...
└── nnUNet_results

```

nnUNet requires that environment variables be set before any commands are executed. To temporarily set them, run the following:

```sh
export nnUNet_raw_data_base="/OUTPUT_DIRECTORY/nnUNet_raw_data_base"
export nnUNet_raw="/OUTPUT_DIRECTORY/nnUNet_raw"
export nnUNet_preprocessed="/OUTPUT_DIRECTORY/nnUNet_preprocessed"
export RESULTS_FOLDER="/OUTPUT_DIRECTORY/nnUNet_trained_models"
export nnUNet_results=="/OUTPUT_DIRECTORY/nnUNet_results"
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```

To permanently set these environment variables, make sure that in your `~/.bashrc` file, these environment variables are set for nnUNet. The `nnUNet_preprocessed` and `nnUNet_trained_models` folders are generated as empty folders for you by Med-ImageTools. `nnUNet_raw_data_base` is populated with the required raw data files. Add this to the file:
To permanently set these environment variables, make sure that in your `~/.bashrc` file, these environment variables are set for nnUNet. The `nnUNet_preprocessed` and `nnUNet_results` folders are generated as empty folders for you by Med-ImageTools. `nnUNet_raw` is populated with the required raw data files. Add this to the file:

```sh
export nnUNet_raw_data_base="/OUTPUT_DIRECTORY/nnUNet_raw_data_base"
export nnUNet_raw="/OUTPUT_DIRECTORY/nnUNet_raw"
export nnUNet_preprocessed="/OUTPUT_DIRECTORY/nnUNet_preprocessed"
export RESULTS_FOLDER="/OUTPUT_DIRECTORY/nnUNet_trained_models"
export nnUNet_results=="/OUTPUT_DIRECTORY/nnUNet_results"
```

Then, execute the command:
Expand All @@ -185,18 +70,18 @@ Then, execute the command:
source ~/.bashrc
```

Too allow nnUNet to preprocess your data for training, run the following command. Set XXX to the ID that you want to preprocess. This is your task ID. For example, for Task500_HNSCC, the task ID is 500. Task IDs must be between 500 and 999, so Med-ImageTools can run 500 instances with the `--nnunet` flag in a single output folder.
Too allow nnUNet to preprocess your data for training, run the following command. Set X to the ID that you want to preprocess. This is your dataset ID. For example, for Dataset001_HNSCC, the dataset ID is 1. Dataset IDs must be between 1 and 999, so Med-ImageTools can run 999 instances with the `--nnunet` flag in a single output folder.

```sh
nnUNet_plan_and_preprocess -t XXX --verify_dataset_integrity
nnUNetv2_plan_and_preprocess -d X --verify_dataset_integrity -c 3d_fullres
```

### nnUNet Training

Once nnUNet has finished preprocessing, you may begin training your nnUNet model. To train your model, run the following command. Learn more about nnUNet's options here: <https://github.com/MIC-DKFZ/nnUNet#model-training>
Once nnUNet has finished preprocessing, you may begin training your nnUNet model. To train your model, run the following command. Learn more about nnUNet's options here: <https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/how_to_use_nnunet.md>

```sh
nnUNet_train CONFIGURATION TRAINER_CLASS_NAME TASK_NAME_OR_ID FOLD
nnUNetv2_train nnUNetv2_train DATASET_NAME_OR_ID UNET_CONFIGURATION FOLD
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```

## nnUNet Inference
Expand All @@ -219,14 +104,14 @@ The directory structue will look like:

```sh
OUTPUT_DIRECTORY
├── 0_subject1_0000.nii.gz
├── subject1_000_0000.nii.gz
└── ...
```

To run inference, run the command:

```sh
nnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m CONFIGURATION
nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_NAME_OR_ID -c CONFIGURATION
```

In this case, the `INPUT_FOLDER` of nnUNet is the `OUTPUT_DIRECTORY` of Med-ImageTools.
In this case, the `INPUT_FOLDER` of nnUNet is the `OUTPUT_DIRECTORY` of Med-ImageTools.
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