论文标题
部分可观测时空混沌系统的无模型预测
Automated GI tract segmentation using deep learning
论文作者
论文摘要
辐射肿瘤学家的工作是提供指向肿瘤的X射线梁，同时避免胃和肠。使用MR-LINACS（磁共振成像和线性加速器系统），肿瘤学家可以可视化肿瘤的位置，并根据肿瘤细胞的存在允许精确剂量，这种肿瘤细胞的存在可能每天变化。当前概述胃和肠的位置以调节X射线束方向以避免器官的剂量递送到肿瘤。这是一个耗时且劳动密集型的过程，除非深度学习方法可以自动化分段过程，否则可以轻松地将15分钟的治疗方法延长至一个小时。本文讨论了使用深度学习的自动分割过程，以使该过程更快，并允许更多患者获得有效的治疗。
The job of Radiation oncologists is to deliver x-ray beams pointed toward the tumor and at the same time avoid the stomach and intestines. With MR-Linacs (magnetic resonance imaging and linear accelerator systems), oncologists can visualize the position of the tumor and allow for precise dose according to tumor cell presence which can vary from day to day. The current job of outlining the position of the stomach and intestines to adjust the X-ray beams direction for the dose delivery to the tumor while avoiding the organs. This is a time-consuming and labor-intensive process that can easily prolong treatments from 15 minutes to an hour a day unless deep learning methods can automate the segmentation process. This paper discusses an automated segmentation process using deep learning to make this process faster and allow more patients to get effective treatment.
