Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models

This repository contains the pytorch implementation of our recent paper:

"Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models"

The code is written by Xin Yu and adapted from steveli/partial-encoder-decoder

Data preparation

The axial slice are downsampled to 256 $\times$ 256 and saved in .png format. Target slice of each subject need to be selected. image pairs (conditonal, target) information should be saved in .csv file as the format in example csv

Results

Citation

If you find our work relevant to your research, please cite:

@inproceedings{yu2022reducing,
  title={Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models},
  author={Yu, Xin and Yang, Qi and Tang, Yucheng and Gao, Riqiang and Bao, Shunxing and Cai, Leon Y and Lee, Ho Hin and Huo, Yuankai and Moore, Ann Zenobia and Ferrucci, Luigi and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={202--212},
  year={2022},
  organization={Springer}
}