Atlas-based automatic segmentation of MR images: Validation study on the brainstem in radiotherapy context
Atlas-based automatic segmentation of MR images: Validation study on the brainstem in radiotherapy context
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DOI：
10.1016/j.ijrobp.2004.08.055
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发表时间：
2005-01-01
影响因子：
7
通讯作者：
Ayache, N
中科院分区：
文献类型：
--
作者：
Bondiau, PY;Malandain, G;Ayache, N
目的：脑肿瘤放射治疗需要对几个单独的脑结构进行体积测量和定位。任何可以帮助医生进行描绘的工具都会有很大的帮助。在分割方法中，基于图谱的方法很有吸引力，因为它们能够同时分割多个结构，同时保留解剖拓扑结构。本研究的目的是评估这样一个方法在临床context.Methods和材料：脑图谱是由两个三维（3D）卷：第一个是人工3D磁共振成像（MRI）;第二个由分段结构在这个人工MRI。人造3D MRI相对于患者3D MRI数据集的弹性配准产生可以应用于标记图像的弹性变换。弹性变换是通过最小化图像强度的平方差之和而获得的，并从光流原理导出。该自动描绘（AD）使得能够将分割的结构映射到患者MRI上。AD的参数已经在一组20名患者上进行了优化。结果获得了一系列的6名患者的MRI。一个全面的验证AD已经进行了临床上的体积，位置，灵敏度和特异性，由一个小组的7名实验医生进行比较的体积，位置，灵敏度和特异性的基于图谱的分割性能的脑tumor treatments.Results：专家观察者间的体积变异范围从16.70 cm（3）到41.26 cm（3）。对于患者，最小体积与最大体积的比率范围为48%至70%。中位体积从19.47 cm（3）到27.66 cm（3）不等，通过AD计算的脑干体积从17.75 cm（3）到24.54 cm（3）不等。专家的敏感性和特异性中位数分别为0.75 - 0.98和0.85 - 0.99。AD的中位数分别为0.77和0.97。专家的平均值范围分别为0.78至0.97和0.86至0.99。平均AD值分别为0.76和0.97。结论：该方法具有良好的可重复性，在准确性和鲁棒性之间取得了良好的平衡，并导致了可重复的分割和标记。这些结果可以通过用肿瘤的粗略信息丰富图谱或对不同的结构使用不同的变形规律来改善。定性结果还表明，该方法可用于自动分割的其他器官，如颈部，胸部，腹部，骨盆和四肢。(C)2005年爱思唯尔公司
Purpose: Brain tumor radiotherapy requires the volume measurements and the localization of several individual brain structures. Any tool that can assist the physician to perform the delineation would then be of great help. Among segmentation methods, those that are atlas-based are appealing because they are able to segment several structures simultaneously, while preserving the anatomy topology. This study aims to evaluate such a method in a clinical context.Methods and Materials: The brain atlas is made of two three-dimensional (3D) volumes: the first is an artificial 3D magnetic resonance imaging (MRI); the second consists of the segmented structures in this artificial MRI. The elastic registration of the artificial 3D MRI against a patient 3D MRI dataset yields an elastic transformation that can be applied to the labeled image. The elastic transformation is obtained by minimizing the sum of the square differences of the image intensities and derived from the optical flow principle. This automatic delineation (AD) enables the mapping of the segmented structures onto the patient MRI. Parameters of the AD have been optimized on a set of 20 patients. Results are obtained on a series of 6 patients' MRI. A comprehensive validation of the AD has been conducted on performance of atlas-based segmentation in a clinical context with volume, position, sensitivity, and specificity that are compared by a panel of seven experimented physicians for the brain tumor treatments.Results: Expert interobserver volume variability ranged from 16.70 cm(3) to 41.26 cm(3). For patients, the ratio of minimal to maximal volume ranged from 48% to 70%. Median volume varied from 19.47 cm(3) to 27.66 cm(3) and volume of the brainstem calculated by AD varied from 17.75 cm(3) to 24.54 cm(3). Medians of experts ranged, respectively, for sensitivity and specificity, from 0.75 to 0.98 and from 0.85 to 0.99. Median of AD were, respectively, 0.77 and 0.97. Mean of experts ranged, respectively, from 0.78 to 0.97 and from 0.86 to 0.99. Mean of AD were, respectively, 0.76 and 0.97.Conclusions: Results demonstrate that the method is repeatable, provides a good trade-off between accuracy and robustness, and leads to reproducible segmentation and labeling. These results can be improved by enriching the atlas with the rough information of tumor or by using different laws of deformation for the different structures. Qualitative results also suggest that this method can be used for automatic segmentation of other organs such as neck, thorax, abdomen, pelvis, and limbs. (C) 2005 Elsevier Inc.