Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.
IEEE Trans Med Imaging. 2010 Dec;29(12):2023-37. doi: 10.1109/TMI.2010.2058861. Epub 2010 Jul 19.
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.
一种用于同时分割多个相互作用的表面的新方法，称为 LOGISMOS（分层最优多物体和表面的图形分割），被报道。该方法基于在单个 n 维图形中算法地合并多个空间相互关系，然后进行图形优化，从而得到全局最优解。LOGISMOS 方法在人体膝关节的骨和软骨分割任务中得到了验证。尽管仅在相对较小的九个示例图像上进行了训练，但该系统仍取得了良好的性能。通过使用留一法测试进行的骰子相似系数（DSC）判断，股骨、胫骨和髌骨软骨区域的 DSC 值分别为 0.84 ± 0.04、0.80 ± 0.04 和 0.80 ± 0.04。考虑到软骨区域的窄片特征，这些都是非常优秀的 DSC 值。同样，与 Osteoarthritis Initiative 数据库中的 60 个随机选择的 3-D MR 图像数据集的手动跟踪独立标准相比，获得的低签名平均软骨厚度误差较小-股骨、胫骨和髌骨软骨厚度的分别为 0.11 ± 0.24、0.05 ± 0.23 和 0.03 ± 0.17 mm。六个检测到的表面的平均签名表面定位误差范围为 0.04 ± 0.12 mm 至 0.16 ± 0.22 mm。所报道的 LOGISMOS 框架提供了股骨、胫骨和髌骨的膝关节骨和软骨表面的强大而准确的分割。作为一种通用的分割工具，所开发的框架可以应用于广泛的多目标多表面分割问题。
