A geodesic deformable model for automatic segmentation of image sequences applied to radiation therapy
A geodesic deformable model for automatic segmentation of image sequences applied to radiation therapy
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DOI：
10.1007/s11548-010-0513-9
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发表时间：
2011-05-01
影响因子：
3
通讯作者：
Delgado, J. M.
中科院分区：
文献类型：
--
作者：
Bueno, G.;Deniz, O.;Delgado, J. M.
目的 图像引导分割放射治疗应考虑器官运动。开发了一种可变形分割和配准方法，用于分次间和分次内器官运动规划和评估。方法合成能量最小化主动模型，用于跟踪放射治疗中由感兴趣区域（ROI）描绘的一组器官。初始模型由一个表面组成，该表面经过变形以通过几何属性匹配 ROI 轮廓，遵循热流模型。使用 Shepp-Logan 头部 CT 模拟测试可变形分割模型，并应用不同的定量指标，例如 ROC 分析、Jaccard 指数、Dice 系数和 Hausdorff 距离。 结果 对心脏、胸部和骨盆区域进行了自动分割与手动分割的实验评估。该方法已经过定量验证，灵敏度和特异度平均分别为 93.3% 和 99.2%，Jaccard 指数平均为 90.79%，Dice 系数平均为 95.15%，Hausdorff 距离平均为 0.96% mm。 结论 开发了基于模型的可变形分割，并针对图像引导放疗治疗计划进行了测试。该方法高效、稳健，并且对于无标记的 2D CT 数据具有足够的准确性。
Purpose Organ motion should be taken into account for image-guided fractionated radiotherapy. A deformable segmentation and registration method was developed for inter- and intra-fraction organ motion planning and evaluation.Methods Energy minimizing active models were synthesized for tracking a set of organs delineated by regions of interest (ROI) in radiotherapy treatment. The initial model consists of a surface deformed to match the ROI contour by geometrical properties, following a heat flow model. The deformable segmentation model was tested using a Shepp-Logan head CT simulation, and different quantitative metrics were applied such as ROC analysis, Jaccard index, Dice coefficient and Hausdorff distance.Results Experimental evaluation of automated versus manual segmentation was done for the cardiac, thoracic and pelvic regions. The method has been quantitatively validated, obtaining an average of 93.3 and 99.2% for the sensitivity and specificity, respectively, 90.79% for the Jaccard index, 95.15% for the Dice coefficient and 0.96% mm for the Hausdorff distance.Conclusions Model-based deformable segmentation was developed and tested for image-guided radiotherapy treatment planning. The method is efficient, robust and has sufficient accuracy for 2D CT data without markers.