Dura Esther, Domingo Juan, Ayala Guillermo, Marti-Bonmati Luis, Goceri E
Department of Informatics, School of Engineering, University of Valencia, Avda. de la Universidad, 46100, Burjasot, Spain.
Department of Statistics and Operations Research, University of Valencia, Avda. Vicent Andrés Estellés, 1, 46100, Burjasot, Spain.
Biomed Eng Online. 2017 Jan 13;16(1):15. doi: 10.1186/s12938-016-0305-8.
Anatomical atlases are 3D volumes or shapes representing an organ or structure of the human body. They contain either the prototypical shape of the object of interest together with other shapes representing its statistical variations (statistical atlas) or a probability map of belonging to the object (probabilistic atlas). Probabilistic atlases are mostly built with simple estimations only involving the data at each spatial location.
A new method for probabilistic atlas construction that uses a generalized linear model is proposed. This method aims to improve the estimation of the probability to be covered by the liver. Furthermore, all methods to build an atlas involve previous coregistration of the sample of shapes available. The influence of the geometrical transformation adopted for registration in the quality of the final atlas has not been sufficiently investigated. The ability of an atlas to adapt to a new case is one of the most important quality criteria that should be taken into account. The presented experiments show that some methods for atlas construction are severely affected by the previous coregistration step.
We show the good performance of the new approach. Furthermore, results suggest that extremely flexible registration methods are not always beneficial, since they can reduce the variability of the atlas and hence its ability to give sensible values of probability when used as an aid in segmentation of new cases.
解剖图谱是表示人体器官或结构的三维体积或形状。它们包含感兴趣对象的原型形状以及表示其统计变化的其他形状（统计图谱）或属于该对象的概率图（概率图谱）。概率图谱大多仅通过涉及每个空间位置数据的简单估计构建。
提出了一种使用广义线性模型构建概率图谱的新方法。该方法旨在改进肝脏覆盖概率的估计。此外，构建图谱的所有方法都涉及对可用形状样本进行预先配准。用于配准的几何变换对最终图谱质量的影响尚未得到充分研究。图谱适应新病例的能力是应考虑的最重要质量标准之一。所展示的实验表明，一些图谱构建方法受到先前配准步骤的严重影响。
我们展示了新方法的良好性能。此外，结果表明，极其灵活的配准方法并不总是有益的，因为它们可能会降低图谱的可变性，从而降低其在辅助新病例分割时给出合理概率值的能力。
