RTDosePrediction

Automatic Radiotherapy Treatment Planning ， Knowledge-Based Planning ， Dose Prediction ， Cascade 3D Network (C3D) ，DCNN, Head and Neck , 1st Place Solution to the AAPM OpenKBP challenge

Please feel free to concat me if you have any questions， email: [email protected], Shuolin Liu

Overview

This repository contains an PyTorch implementation for radiotherapy dose prediction, along with pre-trained models and examples.

The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. This implementation is a work in progress -- more dose prediction models are currently being implemented. Currently support：

C3D, a cascade 3D network for radiotherapy dose prediction, the 1st place solution to the AAPM OpenKBP challenge
DCNN, a lightweight and accurate dose prediction method

Performance

Results on OpenKBP Test Set using a Single model with test-time augmenation(TTA)

Model	Batch size	GPU memory	Training iterations	Training time	Dose score	DVH score	Pre-trained Models
C3D (3D)	2	18Gb	80,000	50 hours (Two 1080TIs)	2.46	1.46	Google Drive Baidu Drive, PassWord：voni
DCNN (2D)	32	3Gb	100,000	20 hours (Single 1080TI)	2.75	1.68	Google Drive Baidu Drive, PassWord：j56y

OpenKBP leaderboard

Requirements

torch >=1.2.0
tqdm
opencv-python
numpy
SimpleITK
pandas
scikit-image
scipy

Usage

Data Preparation
- Download OpenKBP challenge repository, and copy the repository to
  /path_to_your_RTDosePrediction/RTDosePrediction/Data/
For me, /path_to_your_RTDosePrediction/ is E://Project/RTDosePrediction-main/
- C3D：
```
cd /path_to_your_RTDosePrediction/RTDosePrediction/Src/DataPrepare
python prepare_OpenKBP_C3D.py
```
- DCNN：
```
cd /path_to_your_RTDosePrediction/RTDosePrediction/Src/DataPrepare
python prepare_OpenKBP_DCNN.py
```

Training

C3D：
```
cd /path_to_your_RTDosePrediction/RTDosePrediction/Src/C3D
python train.py --batch_size 2 --list_GPU_ids 1 0 --max_iter 80000
```
If you have more GPUs， we suggest set batch_size to 4 and reset list_GPU_ids.
For example, python train.py --batch_size 4 --list_GPU_ids 3 2 1 0 --max_iter 80000

DCNN：

cd /path_to_your_RTDosePrediction/RTDosePrediction/Src/DCNN
python train.py --batch_size 32 --list_GPU_ids 0 --max_iter 100000

Testing

C3D：

cd /path_to_your_RTDosePrediction/RTDosePrediction/Src/C3D
python test.py --GPU_id 0

DCNN：

cd /path_to_your_RTDosePrediction/RTDosePrediction/Src/DCNN
python test.py --GPU_id 0

Using pre-trained models

Download model weights for C3D (Google Drive, Baidu Drive, PassWord：voni) and DCNN(Google Drive, Baidu Drive, PassWord：j56y)
Copy model weights to /path_to_your_RTDosePrediction/RTDosePrediction/PretrainedModels

C3D：

cd /path_to_your_RTDosePrediction/RTDosePrediction/Src/C3D
python test.py --GPU_id 0 --model_path ../../PretrainedModels/C3D_bs4_iter80000.pkl

DCNN：

cd /path_to_your_RTDosePrediction/RTDosePrediction/Src/DCNN
python test.py --GPU_id 0 --model_path ../../PretrainedModels/DCNN_bs32_iter100000.pkl

Citation

if you find C3D and DCNN useful in your research, please consider citing:

@article{C3D,
   title = {Cascade 3D Network for Radiotherapy Dose Prediction : 1st Place Solution to OpenKBP Challenge},
   author = {Shuolin Liu and Jingjing Zhang and Teng Li and Hui Yan  and Jianfei Liu},
   journal = {Medical Physics, under review}
}

DCNN

@article{DCNN,
   title = {Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions},
	doi = {10.1088/1361-6560/aba87b},
	url = {https://doi.org/10.1088%2F1361-6560%2Faba87b},
	year = 2020,
	month = {oct},<br>
	publisher = {{IOP} Publishing},
	volume = {65},
	number = {20},
	pages = {205013},
	author = {Jingjing Zhang and Shuolin Liu and Hui Yan and Teng Li and Ronghu Mao and Jianfei Liu},
	journal = {Physics in Medicine {\&} Biology
}

Acknowledgement

Thank OpenKBP Organizers： Aaron Babier, Binghao Zhang, Rafid Mahmood, Timothy Chan， Andrea McNiven， Thomas Purdie， and Kevin Moore.

https://github.com/ababier/open-kbp

yangsenwxy / rtdoseprediction Goto Github PK