Pohl Kilian M, Fisher John, Shenton Martha, McCarley Robert W, Grimson W Eric L, Kikinis Ron, Wells William M
Surgical Planning Laboratory, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):955-63. doi: 10.1007/11866763_117.
The concept of the Logarithm of the Odds (LogOdds) is frequently used in areas such as artificial neural networks, economics, and biology. Here, we utilize LogOdds for a shape representation that demonstrates desirable properties for medical imaging. For example, the representation encodes the shape of an anatomical structure as well as the variations within that structure. These variations are embedded in a vector space that relates to a probabilistic model. We apply our representation to a voxel based segmentation algorithm. We do so by embedding the manifold of Signed Distance Maps (SDM) into the linear space of LogOdds. The LogOdds variant is superior to the SDM model in an experiment segmenting 20 subjects into subcortical structures. We also use LogOdds in the non-convex interpolation between space conditioned distributions. We apply this model to a longitudinal schizophrenia study using quadratic splines. The resulting time-continuous simulation of the schizophrenic aging process has a higher accuracy then a model based on convex interpolation.
对数几率（LogOdds）的概念在人工神经网络、经济学和生物学等领域经常被使用。在此，我们将LogOdds用于一种形状表示，该表示展示了医学成像所需的特性。例如，这种表示对解剖结构的形状以及该结构内的变化进行编码。这些变化被嵌入到与概率模型相关的向量空间中。我们将我们的表示应用于基于体素的分割算法。我们通过将符号距离映射（SDM）的流形嵌入到LogOdds的线性空间中来实现这一点。在将20名受试者分割为皮质下结构的实验中，LogOdds变体优于SDM模型。我们还将LogOdds用于空间条件分布之间的非凸插值。我们将此模型应用于使用二次样条的纵向精神分裂症研究。由此产生的精神分裂症衰老过程的时间连续模拟比基于凸插值的模型具有更高的准确性。
