Department of Electrical Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT 06520, USA.
IEEE Trans Med Imaging. 2012 Jun;31(6):1213-27. doi: 10.1109/TMI.2012.2186976. Epub 2012 Feb 6.
External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician.
外部束放射治疗（EBRT）可用于治疗癌症，将放射剂量精确地施加于癌变区域。然而，在治疗过程中，软组织会发生变形，例如在宫颈癌中，这为靶区和其他感兴趣结构的勾画带来了重大挑战。此外，肿瘤等病变的存在和消退可能违反配准约束，导致配准错误。在本文中，我们使用统一的贝叶斯框架同时解决了自动分割、非刚性配准和宫颈磁共振（MR）数据中的肿瘤检测问题。所提出的新方法可以在逐步根据所获得的非刚性变换识别感兴趣器官的边界的同时生成肿瘤概率图。该方法能够处理肿瘤显著消退及其对周围组织的影响等挑战。我们在 6 名患者的 36 组 MR 数据上对新方法和各种现有的算法进行了比较，每位患者都有 6 个 T2 加权的宫颈 MR 图像。结果表明，与手动分割相比，所提出的方法具有相当的准确性，并且明显优于现有的配准算法。此外，该方法生成的肿瘤检测结果与合格临床医生的手动勾画具有高度一致性。
