LOGISMOS--layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint.
LOGISMOS--layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint.
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DOI：
10.1109/tmi.2010.2058861
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发表时间：
2010-12
影响因子：
10.6
通讯作者：
Sonka M
中科院分区：
文献类型：
--
作者：
Yin Y;Zhang X;Williams R;Wu X;Anderson DD;Sonka M
提出了一种新的多目标多交互曲面同时分割方法——LOGISMOS（分层最优多目标多曲面图形图像分割）。该方法基于在单个n维图中合并多个空间相互关系的算法，然后进行图优化，从而产生全局最优解。LOGISMOS方法的实用性和性能在人体膝关节骨和软骨分割任务中得到了验证。虽然只训练了相对较少的9个示例图像，但该系统取得了良好的性能。股骨、胫骨、髌骨软骨区域的DSC值分别为0.84±0.04、0.80±0.04、0.80±0.04。考虑到软骨区域的窄片特征，这些是很好的DSC值。同样，与骨关节炎协会数据库中随机选择的60个3d MR图像数据集的手工追踪独立标准相比，低符号的平均软骨厚度误差为：股骨、胫骨和髌骨软骨厚度分别为0.11±0.24、0.05±0.23和0.03±0.17 mm。6个检测表面的平均签名面定位误差范围为0.04±0.12 mm ~ 0.16±0.22 mm。报道的LOGISMOS框架提供了膝关节骨和股骨、胫骨和髌骨软骨表面的稳健和准确的分割。该框架作为一种通用的分割工具，可广泛应用于多目标多曲面的分割问题。
A novel method for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects, called LOGISMOS (layered optimal graph image segmentation of multiple objects and surfaces), is reported. The approach is based on the algorithmic incorporation of multiple spatial inter-relationships in a single n-dimensional graph, followed by graph optimization that yields a globally optimal solution. The LOGISMOS method’s utility and performance are demonstrated on a bone and cartilage segmentation task in the human knee joint. Although trained on only a relatively small number of nine example images, this system achieved good performance. Judged by dice similarity coefficients (DSC) using a leave-one-out test, DSC values of 0.84 ± 0.04, 0.80 ± 0.04 and 0.80 ± 0.04 were obtained for the femoral, tibial, and patellar cartilage regions, respectively. These are excellent DSC values, considering the narrow-sheet character of the cartilage regions. Similarly, low signed mean cartilage thickness errors were obtained when compared to a manually-traced independent standard in 60 randomly selected 3-D MR image datasets from the Osteoarthritis Initiative database—0.11 ± 0.24, 0.05 ± 0.23, and 0.03 ± 0.17 mm for the femoral, tibial, and patellar cartilage thickness, respectively. The average signed surface positioning errors for the six detected surfaces ranged from 0.04 ± 0.12 mm to 0.16 ± 0.22 mm. The reported LOGISMOS framework provides robust and accurate segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multiobject multisurface segmentation problems.