Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee Images.
Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee Images.
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DOI：
10.1109/mmbia.2012.6164757
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Niethammer M
中科院分区：
文献类型：
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作者：
Shan L;Charles C;Niethammer M
本文提出了一种建立膝关节骨软骨图谱的方法，并利用该图谱从T1加权磁共振图像中自动分割股骨软骨和胫骨软骨。将各向异性空间正则化纳入到三标签分割框架中，以改善薄软骨层的分割结果。我们在分割方法中联合使用了地图集信息和概率k近邻分类器的输出。由此产生的软骨分割方法是全自动的。对来自骨关节炎研究数据集的18张膝关节MR图像进行人工专家分割的验证结果表明，股骨软骨和胫骨软骨的分割效果良好（平均Dice相似系数分别为78.2%和82.6%）。
This paper proposes a method to build a bone-cartilage atlas of the knee and to use it to automatically segment femoral and tibial cartilage from T1 weighted magnetic resonance (MR) images. Anisotropic spatial regularization is incorporated into a three-label segmentation framework to improve segmentation results for the thin cartilage layers. We jointly use the atlas information and the output of a probabilistic k nearest neighbor classifier within the segmentation method. The resulting cartilage segmentation method is fully automatic. Validation results on 18 knee MR images against manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 78.2% and 82.6% respectively).