Chen Ting, Kim Sung, Zhou Jinghao, Metaxas Dimitris, Rajagopal Gunaretnam, Yue Ning
Bioinformatics Core, Cancer Institute of New Jersey, University of Medicine and Dentistry of New Jersey, New Brunswick, NJ, USA.
Med Image Comput Comput Assist Interv. 2009;12(Pt 1):43-50. doi: 10.1007/978-3-642-04268-3_6.
Image Guided Radiation Therapy (IGRT) improves radiation therapy for prostate cancer by facilitating precise radiation dose coverage of the object of interest, and minimizing dose to adjacent normal organs. In an effort to optimize IGRT, we developed a fast segmentation-registration-segmentation framework to accurately and efficiently delineate the clinically critical objects in Cone Beam CT images obtained during radiation treatment. The proposed framework started with deformable models automatically segmenting the prostate, bladder, and rectum in planning CT images. All models were built around seed points and involved in the CT image under the influence of image features using the level set formulation. The deformable models were then converted into meshless point sets and underwent a 3D non rigid registration from the planning CT to the treatment CBCT. The motion of deformable models during the registration was constrained by the global shape prior on the target surface during the deformation. The meshless formulation provided a convenient interface between deformable models and the image feature based registration method. The final registered deformable models in the CBCT domain were further refined using the interaction between objects and other available image features. The segmentation results for 15 data sets has been included in the validation study, compared with manual segmentations by a radiation oncologist. The automatic segmentation results achieved a satisfactory convergence with manual segmentations and met the speed requirement for on line IGRT.
图像引导放射治疗（IGRT）通过促进对感兴趣对象的精确放射剂量覆盖，并将对相邻正常器官的剂量降至最低，改善了前列腺癌的放射治疗。为了优化IGRT，我们开发了一种快速分割-配准-分割框架，以准确、高效地描绘放射治疗期间获得的锥形束CT图像中的临床关键对象。所提出的框架首先使用可变形模型在计划CT图像中自动分割前列腺、膀胱和直肠。所有模型都围绕种子点构建，并在图像特征的影响下使用水平集公式参与CT图像。然后将可变形模型转换为无网格点集，并从计划CT到治疗CBCT进行三维非刚性配准。配准过程中可变形模型的运动在变形过程中受到目标表面全局形状先验的约束。无网格公式为可变形模型和基于图像特征的配准方法之间提供了一个方便的接口。在CBCT域中最终配准的可变形模型利用对象之间的相互作用和其他可用图像特征进一步细化。15个数据集的分割结果已纳入验证研究，与放射肿瘤学家的手动分割结果进行比较。自动分割结果与手动分割结果实现了令人满意的收敛，并满足了在线IGRT的速度要求。
