Awesome

Paper

Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study [Jonghun Kim], [Hyunjin Park] <br> Department of Electrical and Computer Engineering Sungkyunkwan University, Suwon, Korea <br> WACV 2024 [paper] [arxiv]<br>

Overview

This repository contains the code for Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study. The model architecture is illustrated below:

Our code was written by applying SPADE, VQ-GAN, and LDM into 3D methods. We would like to thank those who have shared their code. Thanks to everyone who contributed code and models.

• Taming Transformers for High-Resolution Image Synthesis
• Semantic Image Synthesis with SPADE
• Latent Diffusion Models

Our work proceeds in two steps, and each repository contains explanations on the training and inference methods. Please refer to them for more information.

Datasets

We utilized the multi-modal brain tumor segmentation challenge 2021(BraTS 2021) and Information eXtraction From Images (IXI) dataset. Accessible links are provided below.

BraTS 2021: https://www.synapse.org/#!Synapse:syn25829067/wiki/610863

IXI: https://brain-development.org/ixi-dataset/

Pretrained Model

VQGAN stage1: google drive

VQGAN stage2: google drive

Citation

@InProceedings{Kim_2024_WACV,
    author    = {Kim, Jonghun and Park, Hyunjin},
    title     = {Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-Modal Magnetic Resonance Imaging Study},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {7604-7613}
}