Lu Chao, Chelikani Sudhakar, Duncan James S
Department of Electrical Engineering, School of Engineering & Applied Science, Yale University, New Haven, CT, USA.
Inf Process Med Imaging. 2011;22:525-37. doi: 10.1007/978-3-642-22092-0_43.
Image guided external beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose to the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges. Furthermore, the presence of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, we present a unified MAP framework that performs automatic segmentation, nonrigid registration and tumor detection simultaneously. It can generate a tumor probability map while progressively identifing the boundary of an organ of interest based on the achieved transformation. We demonstrate the approach on a set of 30 T2-weighted MR images, and the results show that the approach performs better than similar methods which separate the registration and segmentation problems. In addition, the detection result generated by the proposed method has a high agreement with the manual delineation by a qualified clinician.
图像引导外照射放射治疗（EBRT）用于癌症治疗时，能够将辐射剂量精确地置于癌区。然而，在治疗过程中软组织会发生变形，比如在宫颈癌治疗中，这带来了重大挑战。此外，诸如肿瘤等病变的存在可能会违反配准约束并导致配准误差。在本文中，我们提出了一个统一的最大后验概率（MAP）框架，该框架能同时执行自动分割、非刚性配准和肿瘤检测。它可以生成肿瘤概率图，同时基于所实现的变换逐步确定感兴趣器官的边界。我们在一组30张T2加权磁共振图像上展示了该方法，结果表明该方法比那些将配准和分割问题分开处理的类似方法表现更好。此外，所提方法生成的检测结果与合格临床医生的手动勾勒结果高度吻合。
