Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis

• URL: http://arxiv.org/abs/2408.03035v2
• Date: Fri, 6 Sep 2024 15:52:16 GMT
• Title: Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis
• Authors: Van Phi Nguyen, Tri Nhan Luong Ha, Huy Hieu Pham, Quoc Long Tran,
• Abstract summary: Conditional video diffusion models (CDM) have shown promising results for video synthesis. We present a new method called Free-Echo for generating realistic echocardiograms from a single end-diastolic segmentation map. Our model can generate plausible echocardiograms that are spatially aligned with the input segmentation map, achieving performance comparable to training-based CDMs.
• Score: 0.16874375111244325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}

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