Pohl Kilian M, Fisher John, Kikinis Ron, Grimson W Eric L, Wells William M
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Surgical Planning Laboratory, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA.
Comput Vis Biomed Image Appl (2005). 2005 Oct;3765:489-498. doi: 10.1007/11569541_49.
Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We present an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior information. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. Structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the maximum a posteriori probability estimation problem. We demonstrate the approach on 20 brain magnetic resonance images showing superior performance, particularly in cases where purely image based methods fail.
当不同区域的边界处对比度很小或没有对比度时，基于标准图像的分割方法效果不佳。在这种情况下，分割很大程度上是通过结合基础结构的形状和相对位置的先验知识以及部分可辨别的边界手动进行的。我们提出了一种由相邻结构的协变形状变形引导的自动化方法，这是先验信息的另一个来源。这些变形由形状图谱捕获，使用逻辑函数将其转换为统计模型。在最大后验概率估计问题的期望最大化公式中，同时估计结构边界、解剖标签和图像不均匀性。我们在20幅脑磁共振图像上展示了该方法，显示出卓越的性能，特别是在基于纯图像的方法失败的情况下。
