Autosegmentation for head and neck Radiotherapy

描述(由申请人提供)：我们建议开发、实施和测试在颅内和头颈部癌症患者的治疗计划图像中自动分割结构所需的方法。先进的放射治疗技术需要描绘大脑放射治疗的关键结构，以确定拟议治疗的剂量是否会损害这些结构的功能。在放射肿瘤学治疗计划过程中使用自动分割计算机模块有可能显著提高接受放射治疗的患者的效率、成本效益以及最终的临床结果。这样的系统将解决与目前在治疗计划图像的连续切片上手动描绘正常解剖结构的做法相关的艰巨的劳动力和时间密集型挑战。具体地说，我们建议(1)进一步改进、实施和测试我们在本机构为治疗中小型颅内肿瘤患者而开发的基于图谱的半自动分割算法，(2)开发、实施和测试基于图谱的分割算法来绘制颅内结构和亚结构的轮廓，用于治疗大型占位性颅内肿瘤，以及(3)量化这些方法在临床环境中提供的用户交互时间的减少。

DESCRIPTION (provided by applicant): We propose to develop, implement, and test the methodology required to automate the segmentation of structures in the treatment planning images of patients with intracranial and head- and-neck cancers. Delineating critical structures for radiotherapy of the brain is required for advanced radiotherapy technologies to determine if the dose from the proposed treatment will impair the functionality of the structures. Employing an automatic segmentation computer module in the radiation oncology treatment planning process has the potential to significantly increase the efficiency, cost- effectiveness, and, ultimately, clinical outcome of patients undergoing radiation therapy. Such a system would address the formidable labor- and time-intensive challenges associated with the current practice of manually delineating normal anatomical structures on the serial slices of treatment planning images. Specifically, we propose to (1) further improve, implement, and test the semi-automatic atlas-based segmentation algorithms we have developed at our institution for the contouring of intracranial structures and substructures for the treatment of patients with small to moderate size intracranial tumors, (2) to develop, implement, and test atlas-based segmentation algorithms for the contouring of intracranial structures and substructures for the treatment of patients with large space-occupying intracranial tumors, and (3) to quantify the reduction in user-interaction time afforded by these methods in the clinical setting.