Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis
<|reference_start|>Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis: Conditional video diffusion models (CDM) have shown promising results for video synthesis, potentially enabling the generation of realistic echocardiograms to address the problem of data scarcity. However, current CDMs require a paired segmentation map and echocardiogram dataset. We present a new method called Free-Echo for generating realistic echocardiograms from a single end-diastolic segmentation map without additional training data. Our method is based on the 3D-Unet with Temporal Attention Layers model and is conditioned on the segmentation map using a training-free conditioning method based on SDEdit. We evaluate our model on two public echocardiogram datasets, CAMUS and EchoNet-Dynamic. We show that our model can generate plausible echocardiograms that are spatially aligned with the input segmentation map, achieving performance comparable to training-based CDMs. Our work opens up new possibilities for generating echocardiograms from a single segmentation map, which can be used for data augmentation, domain adaptation, and other applications in medical imaging. Our code is available at \url{https://github.com/gungui98/echo-free}<|reference_end|>
@article{nguyen2024training-free,
    title={Training-Free Condition Video Diffusion Models for single frame
  Spatial-Semantic Echocardiogram Synthesis},
    author={Van Phi Nguyen, Tri Nhan Luong Ha, Huy Hieu Pham, Quoc Long Tran},
    journal={arXiv preprint arXiv:2408.03035},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03035},
    primaryClass={eess.IV cs.CV}
}