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Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis

Reynaud H, Qiao M, Dombrowski M, Day T, Razavi R, Gomez A, Leeson P, Kainz B (2023)

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Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2023

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 14229 LNCS

Pages Range: 142-152

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Vancouver, BC

ISBN: 9783031439988

DOI: 10.1007/978-3-031-43999-5_14

Abstract

Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, causal modelling, and operator training become approachable through synthetic data. Especially, heavily operator-dependant modalities like Ultrasound imaging require robust frameworks for image and video generation. So far, video generation has only been possible by providing input data that is as rich as the output data, e.g., image sequence plus conditioning in → video out. However, clinical documentation is usually scarce and only single images are reported and stored, thus retrospective patient-specific analysis or the generation of rich training data becomes impossible with current approaches. In this paper, we extend elucidated diffusion models for video modelling to generate plausible video sequences from single images and arbitrary conditioning with clinical parameters. We explore this idea within the context of echocardiograms by looking into the variation of the Left Ventricle Ejection Fraction, the most essential clinical metric gained from these examinations. We use the publicly available EchoNet-Dynamic dataset for all our experiments. Our image to sequence approach achieves an R² score of 93%, which is 38 points higher than recently proposed sequence to sequence generation methods. Code and weights are available at https://github.com/HReynaud/EchoDiffusion.

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How to cite

APA:

Reynaud, H., Qiao, M., Dombrowski, M., Day, T., Razavi, R., Gomez, A.,... Kainz, B. (2023). Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis. In Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 142-152). Vancouver, BC, CA: Springer Science and Business Media Deutschland GmbH.

MLA:

Reynaud, Hadrien, et al. "Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis." Proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, BC Ed. Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, Springer Science and Business Media Deutschland GmbH, 2023. 142-152.

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