This is the official code for the CVPRW-DCA in MI 2024 paper "Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling"
Install the dependencies in the requirements.txt. Download and preprocess the data and execute:
python ot_train_3d.py --config ./experiments/CONFIG.yaml --cuda
For the in-of-distribution dataset, refer to the publicly available LIDC-IDRI dataset. We preprocessed the CT scans following the instructions in the X2CT-GAN repo, and resampled the voxel grids into a 64x64x64. You can scale to a higher resolution if you have access you a bigger GPU. For the 2D projections we used Plastimatch and followed the instructions in the MedNeRF repo setting a wider Hounsfield unit range of -1,000 HU to +1,000 HU. We performed this same process for the rest of the datasets (out-of-distribution). Refer to The Cancer Imaging Archive to download such datasets.
@inproceedings{coronafigueroaa24repeat,
author={Corona-Figueroa, Abril and Shum, Hubert P. H. and Willcocks, Chris G.},
booktitle={Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
series={CVPRW '24},
title={Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling},
year={2024},
publisher={IEEE/CVF},
location={Seattle, USA},
}