Echocardiography video synthesis from end diastolic semantic map via
  diffusion model
Phi Nguyen Van Duc Tran Minh Hieu Pham Huy Long Tran Quoc
Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated
significant achievements in various image and video generation tasks, including
the domain of medical imaging. However, generating echocardiography videos
based on semantic anatomical information remains an unexplored area of
research. This is mostly due to the constraints imposed by the currently
available datasets, which lack sufficient scale and comprehensive frame-wise
annotations for every cardiac cycle. This paper aims to tackle the
aforementioned challenges by expanding upon existing video diffusion models for
the purpose of cardiac video synthesis. More specifically, our focus lies in
generating video using semantic maps of the initial frame during the cardiac
cycle, commonly referred to as end diastole. To further improve the synthesis
process, we integrate spatial adaptive normalization into multiscale feature
maps. This enables the inclusion of semantic guidance during synthesis,
resulting in enhanced realism and coherence of the resultant video sequences.
Experiments are conducted on the CAMUS dataset, which is a highly used dataset
in the field of echocardiography. Our model exhibits better performance
compared to the standard diffusion technique in terms of multiple metrics,
including FID, FVD, and SSMI.