{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T05:07:28Z","timestamp":1764997648005,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031732928"},{"type":"electronic","value":"9783031732904"}],"license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73290-4_23","type":"book-chapter","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T06:02:21Z","timestamp":1729576941000},"page":"232-241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Explainable and\u00a0Controllable Motion Curve Guided Cardiac Ultrasound Video Generation"],"prefix":"10.1007","author":[{"given":"Junxuan","family":"Yu","sequence":"first","affiliation":[]},{"given":"Rusi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yongsong","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yanlin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yaofei","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Yuhao","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Han","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"issue":"1","key":"23_CR1","first-page":"1525","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE). Geosci. Model Dev. Discuss. 7(1), 1525\u20131534 (2014)","journal-title":"Geosci. Model Dev. Discuss."},{"unstructured":"Chen, W., et al.: Control-a-video: controllable text-to-video generation with diffusion models. arXiv preprint arXiv:2305.13840 (2023)","key":"23_CR2"},{"doi-asserted-by":"crossref","unstructured":"Faragallah, O.S., et al.: A comprehensive survey analysis for present solutions of medical image fusion and future directions. IEEE Access 9, 11358\u201311371 (2020)","key":"23_CR3","DOI":"10.1109\/ACCESS.2020.3048315"},{"unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)","key":"23_CR4"},{"unstructured":"JCBrouwer: ControlNet3D. https:\/\/github.com\/JCBrouwer\/ControlNet3D. Accessed 6 June 2024","key":"23_CR5"},{"doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: GLIGEN: open-set grounded text-to-image generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22511\u201322521 (2023)","key":"23_CR6","DOI":"10.1109\/CVPR52729.2023.02156"},{"doi-asserted-by":"crossref","unstructured":"Olive\u00a0Pellicer, A., et al.: Synthetic echocardiograms generation using diffusion models. bioRxiv, 2023-11 (2023)","key":"23_CR7","DOI":"10.1101\/2023.11.11.566718"},{"issue":"7802","key":"23_CR8","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","volume":"580","author":"D Ouyang","year":"2020","unstructured":"Ouyang, D., et al.: Video-based AI for beat-to-beat assessment of cardiac function. Nature 580(7802), 252\u2013256 (2020)","journal-title":"Nature"},{"unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)","key":"23_CR9"},{"key":"23_CR10","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/978-3-031-43999-5_14","volume-title":"MICCAI 2023","author":"H Reynaud","year":"2023","unstructured":"Reynaud, H., et al.: Feature-conditioned cascaded video diffusion models for precise echocardiogram synthesis. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14229, pp. 142\u2013152. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43999-5_14"},{"key":"23_CR11","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/978-3-031-16452-1_57","volume-title":"MICCAI 2022","author":"H Reynaud","year":"2022","unstructured":"Reynaud, H., et al.: D\u2019artagnan: counterfactual video generation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13438, pp. 599\u2013609. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_57"},{"doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","key":"23_CR12","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"23_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"doi-asserted-by":"crossref","unstructured":"Shi, X., et\u00a0al.: Motion-I2V: consistent and controllable image-to-video generation with explicit motion modeling. arXiv preprint arXiv:2401.15977 (2024)","key":"23_CR14","DOI":"10.1145\/3641519.3657497"},{"unstructured":"Singer, U., et\u00a0al.: Make-a-video: text-to-video generation without text-video data. arXiv preprint arXiv:2209.14792 (2022)","key":"23_CR15"},{"unstructured":"Unterthiner, T., van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., Gelly, S.: FVD: a new metric for video generation (2019)","key":"23_CR16"},{"doi-asserted-by":"crossref","unstructured":"Van\u00a0Phi, N., Duc, T.M., Hieu, P.H., Long, T.Q.: Echocardiography video synthesis from end diastolic semantic map via diffusion model. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 13461\u201313465. IEEE (2024)","key":"23_CR17","DOI":"10.1109\/ICASSP48485.2024.10446536"},{"unstructured":"Wang, J., Zhang, Y., et\u00a0al.: Boximator: generating rich and controllable motions for video synthesis. arXiv preprint arXiv:2402.01566 (2024)","key":"23_CR18"},{"unstructured":"Wang, X., et al.: VideoComposer: compositional video synthesis with motion controllability. In: Advances in Neural Information Processing Systems, vol. 36 (2024)","key":"23_CR19"},{"issue":"4","key":"23_CR20","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Wu, J.Z., Ge, Y., Wang, X., et\u00a0al.: Tune-a-video: one-shot tuning of image diffusion models for text-to-video generation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7623\u20137633 (2023)","key":"23_CR21","DOI":"10.1109\/ICCV51070.2023.00701"},{"unstructured":"Xing, Z., et al.: A survey on video diffusion models. arXiv preprint arXiv:2310.10647 (2023)","key":"23_CR22"},{"unstructured":"Zhou, H., et\u00a0al.: OnUVS: online feature decoupling framework for high-fidelity ultrasound video synthesis. arXiv preprint arXiv:2308.08269 (2023)","key":"23_CR23"},{"issue":"1","key":"23_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12947-021-00261-2","volume":"19","author":"J Zhou","year":"2021","unstructured":"Zhou, J., Du, M., Chang, S., Chen, Z.: Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis. Cardiovasc. Ultrasound 19(1), 1\u201311 (2021)","journal-title":"Cardiovasc. Ultrasound"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73290-4_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T06:05:45Z","timestamp":1729577145000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73290-4_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,23]]},"ISBN":["9783031732928","9783031732904"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73290-4_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,23]]},"assertion":[{"value":"23 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}