RPCA-UNet

By Binjie Qin, Haohao Mao, Yiming Liu, Jun Zhao, Yisong Lv, Yueqi Zhu, Song Ding, and Xu Chen

This repository is a pytorch implementation of "Robust PCA Unrolling Network for Super-Resolution Vessel Extraction in X-Ray Coronary Angiography".

Note: The dataset is not publically accessed for the research purpose due to the medical ethics review issued by hospital offical, who confirmed that the raw image data should not be given to anyone outside the author team.

Introduction

We propose a novel robust PCA unrolling network with sparse feature selection for super-resolution XCA vessel imaging. Being embedded within a patch-wise spatiotemporal super-resolution framework that is built upon a pooling layer and a convolutional long short-term memory network, the proposed network can not only gradually prune complex vessel-like artefacts and noisy backgrounds in XCA during network training but also iteratively learn and select the high-level spatiotemporal semantic information of moving contrast agents flowing in the XCA-imaged vessels.

Environment

We could ensure that the code is available in such environment.

• RTX 3090 with driver version 470.141.03
• CUDA 11.1
• cuDNN 8.0.4
• conda create -n RPCA python=3.7.0
• source activate RPCA
• pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
• pip install matplotlib
• pip install tqdm
• pip install scipy
• pip install opencv-python
• pip install scikit-learn
• pip install scikit-image

Data Preparation

Put your images(origin images, vessel images, background images) in '.\Data\rawData'. The folder structure should look like

The project
|
└───Data
|   └───rawData
|   |   └───51-32-1(sequence name)
|   |   |   └───ori_1.png
|   |   |   └───ori_2.png
|   |   |   └───...
|   |   |   └───vessel_1.png
|   |   |   └───vessel_2.png
|   |   |   └───...
|   |   |   └───background_1.png
|   |   |   └───background_2.png
|   |   |   └───...
|   |   └───103-25-1
|   |   └───...
|   └───processedData
└───Data_test
|   └───rawData_test
|   |   └───403-16-1
|   |   |   └───...
└───classes
└───network
└───Results
└───...

(Since the dataset is too large to be uploaded into github, Please email the author [email protected] for the access of more dataset.)

Getting Started

1. Run rawDataProcess.py to generate image patches for training. The output will be saved in .\Data\processedData.

2. Run train.py to train the network. The model will be saved in .\Results.

3. Run batch_test.py to obtain the vessel extraction result. The result will be saved in .\Results\test.

4. Run batch_cal_metric.py to calculate the metrics.