On the use of coupled shape priors for segmentation of magnetic resonance images of the knee.
On the use of coupled shape priors for segmentation of magnetic resonance images of the knee.
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DOI：
10.1109/jbhi.2014.2329493
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发表时间：
2015-05
影响因子：
7.7
通讯作者：
Miller EL
中科院分区：
文献类型：
--
作者：
Pang J;Driban JB;McAlindon TE;Tamez-Peña JG;Fripp J;Miller EL
主动轮廓技术在医学图像分割中得到了广泛的应用。大量的工作集中在使用训练数据来构建先验统计模型，该模型特别适用于感兴趣的对象嵌入在杂乱背景中的问题。通常，训练数据由临床专家手动获得的某些器官或结构的整体形状组成。所得到的先验模型在待识别的整个结构或多个结构上均匀地执行分割精度。在本文中，我们考虑了一种新的耦合先验形状模型，该模型被证明可以提供高准确度，特别是在磁共振（MR）图像中股骨和胫骨分割应用最需要精确度的感兴趣区域。对人膝关节MR图像的分割实验结果表明，新的耦合先验形状和方向性边缘力的组合提供了改进的分割性能。此外，新方法允许与现有技术水平等同地准确识别骨髓病变（BML），这是一种与骨关节炎（OA）相关的有前途的生物标志物，但需要显著减少手动交互。
Active contour techniques have been widely employed for medical image segmentation. Significant effort has been focused on the use of training data to build prior statistical models applicable specifically to problems where the objects of interest are embedded in cluttered background. Usually the training data consists of whole shapes of certain organs or structures obtained manually by clinical experts. The resulting prior models enforce segmentation accuracy uniformly over the entire structure or structures to be identified. In this paper, we consider a new coupled prior shape model which is demonstrated to provide high accuracy, specifically in the region of the interest where precision is most needed for the application of the segmentation of the femur and tibia in magnetic resonance (MR) images. Experimental results for the segmentation of MR images of human knees demonstrate that the combination of the new coupled prior shape and a directional edge force provides the improved segmentation performance. Moreover, the new approach allows for equivalent accurate identification of bone marrow lesions (BMLs), a promising biomarker related to osteoarthritis (OA), to the current state of the art but requires significantly less manual interaction.