Low-Dose CT via Transfer Learning from a 2D Trained Network

This repository contains the code for CPCE-3D network introduced in the following paper

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (IEEE TMI)

Installation

Make sure you have Python 2.7 installed, then install TensorFlow and Scikit-learn on your system.

Usage

Prepare the training data

In order to start the training process, please prepare your training data in the following form:

• data: N x D x W x H
• label: N x W x H

Here N, D, W, and H are number, depth, width, and height of the input data, respectively. Each label corresponds to the central slice of input data. Then data and label are stored in a hdh5 file.

Pre-trained VGG model

Please also download the pre-trained VGG model from here. Link updated on Jan 23, 2019.

Training network

python main.py

If you want to use the transfer learning from 2D to 3D, please train a 2D model first. The CPCE-3D model here can automatically deal with 2D input and 3D input with various depths (3, 5, 7, and 9), relying on the input size. A simple 2D model CPCE-2D and its shortcut connection version are added for only 2D case.

Citation

If you found this code or our work useful please cite us

@article{shan20183d,
  title={3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2-D Trained Network},
  author={Shan, Hongming and Zhang, Yi and Yang, Qingsong and Kruger, Uwe and Kalra, Mannudeep K and Sun, Ling and Cong, Wenxiang and Wang, Ge},
  journal={IEEE Transactions on Medical Imaging},
  volume={37},
  number={6},
  pages={1522--1534},
  year={2018},
  publisher={IEEE}
}

Contact

shanh at rpi dot edu

Any discussions, suggestions and questions are welcome!

PhotoAcoustic Data

Download the data from Data after extracting the files, you should get the following folders in the root:

.\frames_split_1024_test
.\frames_split_1024_train

Then use the python scripts

frame2npy_train.py
frame2npy_test.py

To turn this data to .npy format:

.\npy_frames_split_1024_test
.\npy_frames_split_1024_train