# ADPKD-segmentation for determining Total Kidney Volume (TKV)

Autosomal dominant polycystic kidney disease (ADPKD) Segmentation in [PyTorch](https://github.com/pytorch/pytorch)

Project design, data management, and implementation for first version of polycystic kidney work by [Akshay Goel, MD](https://www.linkedin.com/in/akshay-goel-md/).

Follow-up work by researchers at Weill Cornell Medicine and Cornell University.

# Published in Radiology: Artificial Intelligence (RSNA) in 2022

Goel A, Shih G, Riyahi S, Jeph S, Dev H, Hu R, et al. Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI. Radiology: Artificial Intelligence. p. e210205.

Published Online:Feb 16 2022 https://doi.org/10.1148/ryai.210205

# Multiorgan Extention Published in Tomography 2022

Sharbatdaran A, Romano D, Teichman K, Dev H, Raza SI, Goel A, Moghadam MC, Blumenfeld JD, Chevalier JM, Shimonov D, Shih G, Wang Y, Prince MR. Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease. Tomography. 2022; 8(4):1804-1819. https://doi.org/10.3390/tomography8040152. URL: https://www.mdpi.com/1723226

## See additional README files for more info on:

- [Training/Validation data](data/README.md)

- [Inference input data](inference_input/README.md)

- [Saved inference output files](saved_inference/README.md)

- [Addition ensemble extension](addition_ensemble/README.md)

- [Argmax ensemble and multisequence extension](argmax_ensemble/README.md)

## Preliminary Results Presented as Abstract at SIIM 2020

[Convolutional Neural Networks for Automated Segmentation of Autosomal Dominant Polycystic Kidney Disease. Oral presentation at the Society for Imaging Informatics in Medicine 2020, Austin TX](https://cdn.ymaws.com/siim.org/resource/resmgr/siim20/abstracts-research/goel_convolutional_neural_ne.pdf)

## Examples of Performance on Unseen Multi-institute External Data

Inference was performed by [checkpoints/inference.yml](checkpoints/inference.yml) with checkpoint (checkpoints/best_val_checkpoint.pth)

![Example ADPKD MRI Data](adpkd_inference_ext_50.gif)

![Multi-Insitute External Performance](external-data-performance.png)

# Steps to run:

## **Sidenote on Configs and Experiments**

- All experimental objects are defined by YAML configuration files.

- Configuration files are instantiated via [config_utils.py](adpkd_segmentation/config/config_utils.py).

- Select prior experiments can be reproduced via the coresponding config files (see [experiments/configs](experiments/configs)).

- Tensboard runs and Model Checkpoints for these experiments are saved (see [experiments/runs](experiments/runs)).

## **Inference**

#### 1. Install `requirements.txt` and `adpkd-segmentation` package from source.

`pip install -e . -f https://download.pytorch.org/whl/torch_stable.html`

#### A sidenote on installation

- `requirements.txt` and `adpkd-segmentation` is supported for `python 3.8`

- Specifically, the pipeline has been tested on `python 3.8.4` and `python 3.8.5`

- For best results, we recommend installing all packages in a `python 3.8` environment.

- Once `python 3.8.4` or `3.8.5` is installed, create a virtual environment with `virtualenv`

- You may want to reverence the virtual environment documentation:

- [Basic Virtual Environment Tutorial](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#creating-a-virtual-environment) 

- [Package installation for an environment](https://docs.python.org/3/library/venv.html))

However, we provide a short example here:

```

(Windows)

working_dir>pip install virtualenv

working_dir>py -3.8.4 -m Drive:\path\to\environment_name\

Powershell Activation:

working_dir>Drive\path\to\environment_name\Scripts\activate.ps1

Windows Command Line:

working_dir>Drive\path\to\environment_name\Scripts\activate

working_dir>python setup.py install

(Unix/MacOS)

$pip install virtualenv

$python3.8.4 -m venv /path/to/environment_name

$source /path/to/environment_name/bin/activate

$python

```

#### 2. Select an inference config file.

- To build the model for our best results use [checkpoints/inference.yml](checkpoints/inference.yml) which points to coresponding checkpoint [checkpoints/best_val_checkpoint.pth](checkpoints/best_val_checkpoint.pth)

#### 3. Run inference script:

```

$ python  adpkd_segmentation/inference/inference.py -h

usage: inference.py [-h] [--config_path CONFIG_PATH] [-i INFERENCE_PATH] [-o OUTPUT_PATH]

optional arguments:

  -h, --help            show this help message and exit

  --config_path CONFIG_PATH

                        path to config file for inference pipeline

  -i INFERENCE_PATH, --inference_path INFERENCE_PATH

                        path to input dicom data (replaces path in config file)

  -o OUTPUT_PATH, --output_path OUTPUT_PATH

                        path to output location

```

## **Training Pipeline**

#### 1. Install pip packages.

Install from `requirements.txt` (inside some virtual env):

```

pip install -e . -f https://download.pytorch.org/whl/torch_stable.html

```

#### 2. Set up data as described [here](data/README.md).

Note: Depending on the dataloader you may need to create a train / validation / test json file to indicate splits.

#### 3. Select (or create) a config file. See examples at [experiments/configs](experiments/configs)

#### 4. Run training:

```

$ python -m adpkd_segmentation.train --config path_to_config_yaml --makelinks

```

- If using a specific GPU (e.g. device 2):

```

$ CUDA_VISIBLE_DEVICES=2 python -m adpkd_segmentation.train --config path_to_config_yaml --makelinks

```

The `makelinks` flag is optional and needed only once to create symbolic links to the data.

#### 5. Evaluate:

```

$ python -m adpkd_segmentation.evaluate --config path_to_config_yaml --makelinks

```

If using a specific GPU (e.g. device 2):

```

$ CUDA_VISIBLE_DEVICES=2 python -m adpkd_segmentation.evaluate --config path_to_config_yaml --makelinks

```

For generating TKV calculations:

```

$ python -m adpkd_segmentation.evaluate_patients --config path_to_config_yaml --makelinks --out_path output_csv_path

```

## Misc:

- `multi_train.py` can be used to run multiple training runs in a sequence.

- `create_eval_configs.py` is a utility script to create evaluation configs from the starting training config.

  Also done automatically inside `train.py`.

## Contact

For questions or comments please feel free to email me at <akshay.k.goel@gmail.com>.

## Citing

[![DOI](https://zenodo.org/badge/363872703.svg)](https://zenodo.org/badge/latestdoi/363872703)

```

@misc{Goel:2021,

  Author = {Akshay Goel},

  Title = {ADPKD Segmentation in PyTorch},

  Year = {2021},

  Publisher = {GitHub},

  Journal = {GitHub repository},

  Howpublished = {\url{https://github.com/aksg87/adpkd-segmentation-pytorch}}

}

```

## License <a name="license"></a>

Project is distributed under MIT License

## Acknowledgement

Model architecture utilized from [Segmentation Models Pytorch](https://github.com/qubvel/segmentation_models.pytorch) by Pavel Yakubovskiy.

## **Linters and Formatters**

Please apply these prior to any PRs to this repository.

- Linter `flake8` [link](https://flake8.pycqa.org/en/latest/)

- Formatter `black --line-length 79` [link](https://black.readthedocs.io/en/stable/the_black_code_style/current_style.html)

If you use VSCode you can add these to your settings as follows:

```

  "python.formatting.provider": "black",

  "python.linting.flake8Enabled": true,

  "python.formatting.blackArgs": [

    "--experimental-string-processing",    

    "--line-length",

    "79",

  ],

```