bibtext: @InProceedings{ Ngu_TrainingFree_MICCAI2024,
                   author = { Nguyen, Van Phi and Luong Ha, Tri Nhan and Pham, Huy Hieu and Tran, Quoc Long },
                   title = { { Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: Condition 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}
title: Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis
authors: Nguyen, Van Phi Luong Ha, Tri Nhan Pham, Huy Hieu Tran, Quoc Long