Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, methodology and validation on normal subjects
Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, methodology and validation on normal subjects
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DOI：
10.1109/42.811271
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发表时间：
1999-10-01
影响因子：
10.6
通讯作者：
Demaerel, P
中科院分区：
文献类型：
--
作者：
Dawant, BM;Hartmann, SL;Demaerel, P
本文所呈现的研究对以下假设进行了检验：全局相似性变换与局部自由变形相结合可用于对脑部磁共振图像中的内部结构进行精确分割。为了对我们的方法进行定量评估，两位评估者在其中一个体数据（参考体数据）上手动分割了整个大脑、小脑和尾状核头部，并使用计算出的变换将其映射回所有其他体数据。将如此获得的轮廓与在每个个体大脑中围绕感兴趣结构手动绘制的轮廓进行了比较。同两位评估者进行了两次手动描绘，以测试评估者之间和评估者内部的变异性。对于大脑和小脑，结果表明，对于每位评估者，手动获得的轮廓与通过变形其自身图谱自动获得的轮廓实际上无法区分。此外，一位评估者手动获得的轮廓与通过变形该评估者自身图谱自动获得的轮廓比两位评估者手动获得的轮廓更相似。对于尾状核，手动的评估者内部和评估者之间的相似性指标仍然略好于手动与自动指标，这主要是由于本研究中使用的图像的空间分辨率。定性结果还表明，该方法可用于更复杂结构（如海马体）的分割。
The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum, and the head of the caudate have been segmented manually by two raters on one of the volumes (the reference volume) and mapped back onto all the other volumes, using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain, Manual delineation was performed twice by the same two raters to test inter- and intrarater variability. For the brain and the cerebellum, results indicate that for each rater, contours obtained manually and contours obtained automatically by deforming his own atlas are virtually indistinguishable. Furthermore, contours obtained manually by one rater and contours obtained automatically by deforming this rater's own atlas are more similar than contours obtained manually by two raters, For the caudate, manual intra- and interrater similarity indexes remain slightly better than manual versus automatic indexes, mainly because of the spatial resolution of the images used in this study. Qualitative results also suggest that this method can be used for the segmentation of more complex structures, such as the hippocampus.