Fritscher Karl D, Peroni Marta, Zaffino Paolo, Spadea Maria Francesca, Schubert Rainer, Sharp Gregory
Department for Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114.
Paul Scherrer Institut, Villigen 5232, Switzerland.
Med Phys. 2014 May;41(5):051910. doi: 10.1118/1.4871623.
Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented.
The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other.
18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved.
The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.
准确勾画危及器官（OARs）是调强放射治疗的前提条件。然而，手动勾画OARs既耗时又容易出现较高的观察者间差异。然而，由于图像伪影以及不同结构之间的图像对比度较低，头颈部区域结构自动分割的可用方法数量仍然相当少。在本项目中，提出了一种用于头颈部CT图像自动分割的新方法，该方法将基于多图谱分割的稳健性与测地线活动轮廓的灵活性以及统计外观模型提供的先验知识相结合。
所提出的方法使用基于图谱的分割方法并结合标签融合，以便初始化一个基于统计外观模型和测地线活动轮廓的分割流程。基于图谱分割提供的分割结果的解剖学正确近似值用作该近似值迭代细化的起点。最终的分割结果基于模型到图像配准和测地线活动轮廓，它们相互影响。
18幅CT图像以及腮腺和脑干的手动分割标签被用于留一法交叉验证方案，以评估所提出的方法。为此，创建了50种不同的统计外观模型并用于分割。计算了自动分割结果与专家分割之间的骰子系数（DC）、平均绝对距离和最大豪斯多夫距离。可以实现平均骰子系数为DC = 0.81（右腮腺）、DC = 0.84（左腮腺）和DC = 0.86（脑干）。
所提出的框架为头颈部区域的三个重要结构提供了准确的分割结果。与基于使用多个图谱并结合标签融合的分割方法相比，所提出的混合方法在临床可接受的时间内提供了更准确的结果。
