A deformable-model approach to semi-automatic segmentation of CT images demonstrated by application to the spinal canal
A deformable-model approach to semi-automatic segmentation of CT images demonstrated by application to the spinal canal
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DOI：
10.1118/1.1634483
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发表时间：
2004-02-01
期刊：
影响因子：
3.8
通讯作者：
Liao, ZX
中科院分区：
文献类型：
--
作者：
Burnett, SSC;Starkschall, G;Liao, ZX
由于在放射治疗计划中准确定义目标的重要性，我们开发了一种可变形模板算法，用于在计算机断层扫描 (CT) 图像上半自动描绘正常组织结构。我们通过将其应用于椎管来说明该方法。分割分三个步骤进行：(a)通过基于小波的边缘检测获得解剖结构的部分描绘； (b) 通过倒角匹配将可变形模型模板拟合到边缘集； (c) 模板从其原始形状松弛到其最终位置。适当选择模型参数范围可以限制模板的变形，从而考虑患者间的变异性。我们的方法与其他可变形模型中使用的方法不同，因为它本质上不需要对力进行建模。相反，使用从四组手动绘制的轮廓导出的傅立叶描述符对椎管进行建模。在没有人工干预的情况下，对五个 CT 数据集进行了分割，算法的性能由两名放射肿瘤学家主观判断。考虑了两种评估：首先，将随机选择的 100 张轴向 CT 图像的分割与同样随机选择的六名剂量师之一手动绘制的相应轮廓进行比较；在第二次评估中，五个可评估的 CT 集合（总共 557 个轴向图像）中每个图像的分割被评为成功、不成功或需要进一步编辑。由算法生成的轮廓比手动绘制的轮廓更有可能被肿瘤学家认为可接受。可接受轮廓的平均比例为 93%（自动）和 69%（手动）。 91% 的图像自动描绘椎管被认为是成功的，2% 的图像不成功，7% 的图像需要进一步编辑。因此，我们的可变形模板算法可以在 CT 图像上对椎管进行稳健的分割。该方法可以扩展到其他结构，尽管倒角匹配对于描绘被软组织包围的软组织结构是否足够稳健还有待证明。 (C) 2004 年美国医学物理学家协会。
Because of the importance of accurately defining the target in radiation treatment planning, we have developed a deformable-template algorithm for the semi-automatic delineation of normal tissue structures on computed tomography (CT) images. We illustrate the method by applying it to the spinal canal. Segmentation is per-formed in three steps: (a) partial delineation of the anatomic structure is obtained by wavelet-based edge detection; (b) a deformable-model template is fitted to the edge set by chamfer matching; and (c) the template is relaxed away from its original shape into its final position. Appropriately chosen ranges for the model parameters limit the deformations of the template, accounting for interpatient variability. Our approach differs from those used in other deformable models in that it does not inherently require the modeling of forces. Instead, the spinal canal was modeled using Fourier descriptors derived from four sets of manually drawn contours. Segmentation was carried out, without manual intervention, on five CT data sets and the algorithm's performance was judged subjectively by two radiation oncologists. Two assessments were considered: in the first, segmentation on a random selection of 100 axial CT images was compared with the corresponding contours drawn manually by one of six dosimetrists, also chosen randomly; in the second assessment, the segmentation of each image in the five evaluable CT sets (a total of 557 axial images) was rated as either successful, unsuccessful, or requiring further editing. Contours generated by the algorithm were more likely than manually drawn contours to be considered acceptable by the oncologists. The mean proportions of acceptable contours were 93% (automatic) and 69% (manual). Automatic delineation of the spinal canal was deemed to be successful on 91% of the images, unsuccessful on 2% of the images, and requiring further editing on 7% of the images. Our deformable template algorithm thus gives a robust segmentation of the spinal canal on CT images. The method can be extended to other structures, although it remains to be shown that chamfer matching is sufficiently robust for the delineation of soft-tissue structures surrounded by soft tissue. (C) 2004 American Association of Physicists in Medicine.