README.md

<div align="center">

<img src=docs/source/nnDetection.svg width="600px">

![Version](https://img.shields.io/badge/nnDetection-v1.0-blue)
![Python](https://img.shields.io/badge/python-3.8-orange)
![CUDA](https://img.shields.io/badge/CUDA-10.1%2F10.2%2F11.0-green)
![license](https://img.shields.io/badge/License-Apache%202.0-red.svg)

</div>

# What is nnDetection?
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions depend on rating of objects rather than e.g. pixels.
For this task, the cumbersome and iterative process of method configuration constitutes a major research bottleneck. 
Recently, nnU-Net has tackled this challenge for the task of image segmentation with great success.
Following nnU-Net’s agenda, in this work we systematize and automate the configuration process for medical object detection.
The resulting self-configuring method, nnDetection, adapts itself without any manual intervention to arbitrary medical detection problems while achieving results en par with or superior to the state-of-the-art.
We demonstrate the effectiveness of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 10 further public data sets for a comprehensive evaluation of medical object detection methods.

# Installation
1. Install CUDA (>10.1) and cudnn (make sure to select [compatible versions](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html)!)
2. [Optional] Depending on your GPU you might need to set `TORCH_CUDA_ARCH_LIST`, check [compute capabilities](https://developer.nvidia.com/cuda-gpus) here.
3. Install [torch](https://pytorch.org/) (make sure to match the pytorch and CUDA versions!) (requires pytorch >1.7+)
4. Install [torchvision](https://github.com/pytorch/vision) (make sure to match the versions!)
5. Clone nnDetection, `cd [path_to_repo]` and `pip install -e .`
6. Upgrade hydra to next release: `pip install hydra-core --upgrade --pre`
7. Set environment variables (more info can be found below):
    - `det_data`: [required] Path to the source directory where all the data will be located
    - `det_models`: [required] Path to directory where all models will be saved
    - `OMP_NUM_THREADS=1` : [required] Needs to be set! Otherwise bad things will happen... Refer to batchgenerators documentation.
    - `det_num_threads`: [recommended] Number processes to use for augmentation (at least 6, default 12)
    - `det_verbose`: [optional] Can be used to deactivate progress bars (activated by default)
    - `MLFLOW_TRACKING_URI`: [optional] Specify the logging directory of mlflow. Refer to the [mlflow documentation](https://www.mlflow.org/docs/latest/tracking.html) for more information.

Note: nnDetection was developed on Linux => Windows is not supported.

<details close>
<summary>Test Installation</summary>
<br>
Run the following command in the terminal (!not! in pytorch root folder) to verify that the compilation of the C++/CUDA code was successfull:

```bash
python -c "import torch; import nndet._C; import nndet"
```

To test the whole installation please run the Toy Dataset example.
</details>

<details close>
<summary>Maximising Training Speed</summary>
<br>
To get the best possible performance we recommend using CUDA 11.0+ with cuDNN 8.1.X+ and a (!)locally compiled version(!) of Pytorch 1.7+
</details>

<details close>
<summary>Docker Container</summary>
<br>
The provided Dockerfile can be used to setup quick development environments or deploy nnDetection.

Please install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) before continuing.

All projects which are based on nnDetection assume that the base image was build with the following tagging scheme `nnDetection:[version]`.
To build a container (nnDetection Version 0.1) run the following command from the base directory:

```bash
docker build -t nndetection:0.1 .
```

or

```bash
docker build -t nndetection:0.1 --build-arg env_det_num_threads=6 --build-arg env_det_verbose=1 .
```

to overwrite the provided default parameters.

The docker container expects the data and models in `/opt/data` and `/opt/models` respectively.
The directories need to be mounted via docker commands e.g.

```bash
docker run --gpus all -v /path/to/data/on/pc:/opt/data -v /path/to/models/on/pc:/opt/models -it nndetection:0.1 /bin/bash
```

If nnDetection is already configured on the host PC the following command can be used to start the container with the correct paths.

```bash
docker run --gpus all -v ${det_data}:/opt/data -v ${det_models}:/opt/models -it nndetection:0.1 /bin/bash
```

After activating the environment via `. /activate` inside the container, training or inference scripts can be executed with the usual commands (see below).

Warning:
1. The current pytorch versions do not support the 3d conv speed up and thus compiling pytorch from source will run faster than this container.
2. When running a training inside the container it is necessary to [increase the shared memory](https://stackoverflow.com/questions/30210362/how-to-increase-the-size-of-the-dev-shm-in-docker-container).
I tested the following configuration on my local workstation:
```bash
docker run --gpus all -v ${det_data}:/opt/data -v ${det_models}:/opt/models -it --shm-size=24gb nndetection:0.1 /bin/bash
```

</details>

# nnDetection
<div align="center">
    <img src=docs/source/nnDetectionFunctional.svg width="600px">
</div>

<details close>
<summary>nnDetection Module Overview</summary>
<br>
    <div align="center">
        <img src=docs/source/nnDetectionModule.svg width="600px">
    </div>
</details>

<details close>
<summary>nnDetection Functional Details</summary>
<br>
    <div align="center">
        <img src=docs/source/nnDetectionFunctionalDetails.svg width="600px">
    </div>
</details>

# Experiments & Data
The datasets used for our experiments are not hosted or maintained by us, please give credit to the authors of the datasets.
Some of the labels were corrected in datasets which we converted and can be downloaded.
The `Reproducing Experiments` section has an overview of multiple guides which explain the preparation of the datasets.

## Toy Dataset
Running `nndet_example` will automatically generate an example dataset with 3D squares and sqaures with holes which can be used to test the installation or experiment with prototype code (it is still necessary to run the other nndet commands to process/train/predict the dataset).

```bash 
# create data to test installation/environment (10 train 10 test)
nndet_example

# create full dataset for prototyping (1000 train 1000 test)
nndet_example --full [--num_processes]
```

The full problem is very easy and the final results should be near perfect.
After running the generation script follow the `Planning`, `Training` and `Inference` instructions below to construct the whole nnDetection pipeline.

## Reproducing Experiments

<div align="center">

| <!-- --> | <!-- --> | <!-- --> |
|:--------------------------------:|:----------------------:|:----------------------------:|
| [Task 003 Liver](#TODO)          | [Task 011 Kits](#TODO) | [Task 020 RibFrac](#TODO)    |
| [Task 007 Pancreas](#TODO)       | [Task 012 LIDC](#TODO) | [Task 021 ProstateX](#TODO)  |
| [Task 008 Hepatic Vessel](#TODO) | [Task 017 CADA](#TODO) | [Task 025 LymphNodes](#TODO) |
| [Task 010 Colon](#TODO)          | [Task 019 ADAM](#TODO) | [Task 016 Luna](#TODO)       |

</div>

## Adding New Datasets
nnDetection relie on a standardized input format which is very similar to the [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) format and allows easy integration of new datasets.
The format is explained below.

### Folders
All datasets should reside inside `Task[Number]_[Name]` folder inside the specified detection data folder (et the path to this folder with the `det_data` environment flag).
An overview is provided below ([Name] symbolise folder, `-` symbolise files, indents refer to substructures)

```text
${det_data}
    [Task000_Example]
        - dataset.yaml # dataset.json works too
        [raw_splitted_data]
            [imagesTr]
                - case0000_0000.nii.gz # case0000 modality 0
                - case0000_0001.nii.gz # case0000 modality 1
                - case0001_0000.nii.gz # case0001 modality 0
                - case0000_0001.nii.gz # case0001 modality 1
            [labelsTr]
                - case0000.nii.gz # instance segmentation case0000
                - case0000.json # properties of case0000
                - case0001.nii.gz # instance segmentation case0001
                - case0001.json # properties of case0001
            [imagesTs] # optional, same structure as imagesTr
             ...
            [labelsTs] # optional, same structure as labelsTr
             ...
    [Task001_Example1]
        ...
```

### Dataset Info
`dataset.yaml` or `dataset.json` provides general information about the dataset:
Note: [Important] Classes and modalities start with index 0!
```yaml
task: Task000D3_Example

name: "Example" # [Optional]
dim: 3 # number of spatial dimensions of the data
target_class: # define class of interest for patient level evaluations # TODO: check if this should be included
test_labels: True # manually splitted test set

labels: # classes of dataset; need to start at 0
    "0": "Square"
    "1": "SquareHole"

modalities: # modalities of dataset; need to start at 0
    "0": "CT"
```

### Image Format
nnDetection uses the same image format as nnU-Net.
Each case consists of at least one 3D nifty file with one modalityand are saved in the `images` folders.
If multiple modalities are available, each modalities uses a separate file and the sequence at the end of the name indicates the modality (corresponds to the number specified in the dataset file).

An example with two modalities could look like this:
```text
- case001_0000.nii.gz # Case ID: case001; Modality: 0
- case001_0001.nii.gz # Case ID: case001; Modality: 1

- case002_0000.nii.gz # Case ID: case002; Modality: 0
- case002_0001.nii.gz # Case ID: case002; Modality: 1
```

If multiple modalities are available, please check beforehand if they need to be registered and perform registration befor nnDetection preprocessing. nnDetection does (!)not(!) include automatic registration of multiple modalities.

### Label Format
Labels are encoded with two files per case: one nifty file which contains the instance segmentation and one json file which includes the "meta" information of each instance.
The nifty file hould contain all annotated instances where each instance has a unique number and are in consecutive order (e.g. 0 ALWAYS refers to background, 1 refers to the first instance, 2 refers to the second instance ...)
`case[XXXX].json` label files need to provide the class of every instance in the segmentation. In this example the first isntance is assigned to class `0` and the second instance is assigned to class `1`:
```json
{
    "instances": {
        "1": 0,
        "2": 1
    }
}
```

Each label file needs a corresponding json file to define the classes.

## Using nnDetection
The following paragrah provides an high level overview of the functionality of nnDetection and which commands are available.
A typical flow of commands would look like this:
```text
nndet_prep -> nndet_unpack -> nndet_train -> nndet_consolidate -> nndet_predict
```

Eachs of this commands is explained below and more detailt information can be obtained by running `nndet_[command] -h` in the terminal.

### Planning & Preprocessing
Before training the networks, nnDetection needs to preprocess and analyze the data.
The preprocessing stage normalizes and resamples the data while the analyzed properties are used to create a plan which will be used for configuring the training.
nnDetectionV0 requires a GPU with approximately the same amount of VRAM you are planning to use for training (i.e. we used a RTX2080TI; no monitor attached to it) to perform live estimation of the VRAM used by the network.
Future releases aim at improving this process...

```bash
nndet_prep [tasks] [-o / --overwrites]

# Example
nndet_prep 000

# Script
# /experiments/preprocess.py - main()
```

`-o` option can be used to overwrite parameters for planning and preprocessing (refer to the onfig files to see all parameters). A typical usecase is to increase or decrease `prep.num_processes` (number of processes used for cropping) and `prep.num_processes_processing` (number of processes used for resampling) depending on the size/number of modalities of the data and available RAM. The current values are fairly save if 64GB of RAM is available.

After planning and preprocessing the resulting data folder structure should look like this:
```text
[Task000_Example]
    [raw_splitted]
    [raw_cropped] # only needed for different resampling strategies
        [imagesTr] # stores cropped image data; contains npz files
        [labelsTr] # stores labels
    [preprocessed]
        [analysis]
        [properties] # sufficient for new plans
        [labelsTr] # labels in original format (original spacing)
        [labelsTs] # optional
        [Data identifier; e.g. D3V001_3d]
            [imagesTr] # preprocessed data
            [labelsTr] # preprocessed labels (resampled spacing)
        - {name of plan}.pkl e.g. D3V001_3d.pkl
```

Befor starting the training copy the data (Task Folder, dataset info and preprocessed folder are needed) to a SSD (highly recommended) and unpack the image data with

```bash
nndet_unpack [path] [num_processes]

# Example (unpack example with 6 processes)
nndet_unpack ${det_data}/Task000D3_Example/preprocessed/D3V001_3d/imagesTr 6

# Script
# /experiments/utils.py - unpack()
```

### Training and Evaluation
After the planning and preprocessing stage is finished the training phase can be started.
The default setup of nnDetection is trained in a 5 fold cross-validation scheme.
First, check which plans were generated during planning by checking the preprocessing folder and looking for the pickled plan files. In most cases only the defaul plan will be generated (`D3V001_3d`) but there might be instances (e.g. Kits) where the low resolution plan will be generated too (`D3V001LR1_3d`).

```bash
nndet_train [task] [-o / --overwrites] [--sweep]

# Example (train default plan D3V001_3d and search best inference parameters)
nndet_train 000 --sweep

# Script
# /experiments/train.py - train()
```

Use `-o exp.fold=X` to overwrite the trained fold, this should be run for all folds `X = 0, 1, 2, 3, 4`!
The `--sweep` option tells nnDetection to look for the best hyparameters for inference by empirically evaluating them on the validation set.
Sweeping can also be performed later by running the following command:

```bash
nndet_sweep [task] [model] [fold]

# Example (sweep Task 000 of model RetinaUNetV001_D3V001_3d in fold 0)
nndet_sweep 000 RetinaUNetV001_D3V001_3d 0

# Script
# /experiments/train.py - sweep()
```

Evaluation can be invoked by the following command (requires access to model and preprocessed data):
```bash
nndet_eval [task] [model] [fold] [--test] [--case] [--boxes] [--seg] [--instances] [--analyze_boxes]

# Example (evaluate and analyze box predictions of default model)
nndet_eval 000 RetinaUNetV001_D3V001_3d 0 --boxes --analyze_boxes

# Script
# /experiments/train.py - evaluate()

# Note: --test invokes evaluation of the test set
# Note: --seg, --instances are placeholders for future versions and not working yet
```

### Inference
After running all fold it is time to collect the models and creat a unified inference plan.
The following command will copy all the models and predictions per fold and by adding the `sweep` options the empiricaly hyperparameter optimization across all fold can be started.
This will generate a unified plan for all models which will be used during inference.

```bash
nndet_consolidate [task] [model] [--overwrites] [--consolidate] [--num_folds] [--no_model] [--sweep_boxes] [--sweep_instances]

# Example
nndet_consolidate 000 RetinaUNetV001_D3V001_3d --sweep_boxes

# Script
# /experiments/consolidate.py - main()
```

Data which is located in `raw_splitted/imagesTs` will be automatically preprocessed and predicted by running the following command:
```bash
nndet_predict [task] [model] [--fold] [--num_models] [--num_tta] [--no_preprocess]

# Example
nndet_predict 000 RetinaUNetV001_D3V001_3d --fold -1

# Script
# /experiments/predict.py - main()
# Note: --num_models is not supported by default
```

If a self-made test set was used, evaluation can be performed by invoking `nndet_eval` as described above.

## nnU-Net for Detection
TODO

## Pretrained models
TODO

# FAQ
<details close>
<summary>GPU requirements</summary>
<br>
nnDetection v0.1 was developed for GPUs with at least 11GB of VRAM (e.g. RTX2080TI, TITAN RTX).
All of our experiments were conducted with a RTX2080TI.
While the memory can be adjusted by manipulating the correct setting we recommend using the default values for now.
Future releases will refactor the planning stage to improve the VRAM estimation and add support for different memory budgets.
</details>

<details close>
<summary>Error: Undefined CUDA symbols when importing `nndet._C`</summary>
<br>
Please double check CUDA version of your PC, pytorch, torchvision and nnDetection build!
Follow the installation instruction at the beginning!
</details>

<details close>
<summary>Error: No kernel image is available for execution"</summary>
<br>
You are probably executing the build on a machine with a GPU architecture which was not present/set during the build.

Please check [link](https://developer.nvidia.com/cuda-gpus) to find the correct SM architecture and set `TORCH_CUDA_ARCH_LIST`
approriately (e.g. check Dockefile for example).
Make sure to delete all caches before rebulding!
</details>

<details close>
<summary>Training with bounding boxes</summary>
<br>
The first release of nnDetection focuses on 3d medical images and Retina U-Net.
As a consequence training (specifically planning and augmentation) requrie segmentation annotations.
In many cases this limitation can be circumvented by converting the bounding boxes into segmentations.
</details>

<details close>
<summary>Mask RCNN and 2D Datasets</summary>
<br>
2D datasets and Mask R-CNN are not supported in the first release.
We hope to provide these sometime in the future.
</details>

<details close>
<summary>Multi GPU Training</summary>
<br>
Multi GPU training is not officially supported yet.
Inference and the metric computation are not properly designed to support these usecases!
</details>

<details close>
<summary>Prebuild package</summary>
<br>
We are planning to provide prebuild wheels in the future but no prebuild wheels are available right now.
Please use the provided Dockerfile or the installation instructions to run nnDetection.
</details>

# Cite
If you use nnDetection for your project/research/work please cite the following paper:
```text
TODO
```

# Acknowledgements
TODO (medicaldetectiontoolkit, nnunet, torchvision)