An interactive deep-learning method to semi-automatically segment abdominal organs to support stereotactic MR guided online adaptive radiotherapy (SMART) for abdominal cancers

摘要 立体定向MRI引导的在线自适应放射治疗(SMART)是治疗鼻咽癌的有效方法。 胰腺癌和其他上腹部癌症。SMART允许精确交付升级的 避免放疗并发症的腹部肿瘤靶区处方剂量 对肿瘤靶点周围的可移动胃肠道(GI)器官的毒性。在临床上 智能、手动的GI危险橙子(OAR)分割工作流程是最重要的工作流程之一 这很重要，但也是劳动密集度最高的步骤。手动分段需要10分钟 平均，但从5分钟到22分钟不等。缓慢且昂贵的手动分段步骤 直接降低在线智能的可及性和可负担性，并间接降低 SMART由于患者的身体和器官内运动而产生的效果。在这 研究中，我们将开发一个基于深度学习的交互和半自动程序来 准确、快速地分割GI桨，使SMART更高效、更实惠。

ABSTRACT Stereotactic MRI-guided online adaptive radiotherapy (SMART) is an effective treatment for the pancreas and other upper abdominal cancers. SMART allows precise delivery of escalated prescription dose to the abdominal tumor targets while avoiding the complications of radiation toxicity to the mobile gastrointestinal (GI) organs surrounding the tumor target. In the clinical workflow of SMART, manual segmentation of the GI orangs at risk (OARs) is one of the most important but also the most labor-intensive steps. Manual segmentation takes 10 minutes on average but ranges from 5 to 22 minutes. The slow and costly manual segmentation step directly decreases the accessibility and affordability of online SMART and indirectly reduces the effectiveness of SMART due to intra-fractional body and organ movement of the patients. In this study, we will develop a deep-learning based interactive and semi-automatic procedure to accurately and quickly segment the GI OARs to make SMART more efficient and affordable.