Virtual-Contrast-CT

This repo is for the paper - Virtual contrast enhancement for CT scans of abdomen and pelvis

About UNet/FCN PyTorch

This repository is based on PyTorch implementations of U-Net and FCN, which are deep-learning segmentation methods proposed by Ronneberger et al. and Long et al.

• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Fully Convolutional Networks for Semantic Segmentation

Prepare Dataset and DataLoader

You need to prepare the dataset: non-contrast CT and real-contrast CT pairs with the same ID as the input and ground truth.

Define training

Set the path in the training session and run:

• python train.py

Define testing

set the path in the testing session and run:

• python test.py

Model & Test sample

GoogleDrive

Pretrained model

Load pre-train model: pretrained_intensity_model_4level_3c6.pth

Trained model

Try early stage: earlystage_C_32_BCE_HRl5_pix_G_3C_adddreg_s256c224_tw_40.pth