Ersted Rasmussen Mathis, Dueholm Vestergaard Casper, Folsted Kallehauge Jesper, Ren Jintao, Haislund Guldberg Maiken, Nørrevang Ole, Vindelev Elstrøm Ulrik, Sofia Korreman Stine
Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark.
Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, 8200 Aarhus N, Denmark.
Phys Imaging Radiat Oncol. 2024 Jul 2;31:100607. doi: 10.1016/j.phro.2024.100607. eCollection 2024 Jul.
The use of and research in automation and artificial intelligence (AI) in radiotherapy is moving with incredible pace. Many innovations do, however, not make it into the clinic. One technical reason for this may be the lack of a platform to deploy such software into clinical practice. We suggest RadDeploy as a framework for integrating containerized software in clinical workflows outside of treatment planning systems. RadDeploy supports multiple DICOM as input for model containers and can run model containers asynchronously across GPUs and computers. This technical note summarizes the inner workings of RadDeploy and demonstrates three use-cases with varying complexity.
放射治疗中自动化和人工智能（AI）的应用与研究正以前所未有的速度发展。然而，许多创新并未进入临床应用阶段。造成这种情况的一个技术原因可能是缺乏将此类软件应用于临床实践的平台。我们建议将RadDeploy作为一个框架，用于在治疗计划系统之外的临床工作流程中集成容器化软件。RadDeploy支持多种DICOM作为模型容器的输入，并可以跨GPU和计算机异步运行模型容器。本技术说明总结了RadDeploy的内部工作原理，并展示了三个不同复杂程度的用例。
