Risholm Petter, Pieper Steve, Samset Eigil, Wells William M
Harvard Medical School, Brigham & Women's Hospital, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 2):554-61. doi: 10.1007/978-3-642-15745-5_68.
Registration uncertainty may be important information to convey to a surgeon when surgical decisions are taken based on registered image data. However, conventional non-rigid registration methods only provide the most likely deformation. In this paper we show how to determine the registration uncertainty, as well as the most likely deformation, by using an elastic Bayesian registration framework that generates a dense posterior distribution on deformations. We model both the likelihood and the elastic prior on deformations with Boltzmann distributions and characterize the posterior with a Markov Chain Monte Carlo algorithm. We introduce methods that summarize the high-dimensional uncertainty information and show how these summaries can be visualized in a meaningful way. Based on a clinical neurosurgical dataset, we demonstrate the importance that uncertainty information could have on neurosurgical decision making.
当基于配准图像数据做出手术决策时，配准不确定性可能是需要传达给外科医生的重要信息。然而，传统的非刚性配准方法仅提供最可能的变形。在本文中，我们展示了如何通过使用弹性贝叶斯配准框架来确定配准不确定性以及最可能的变形，该框架在变形上生成密集的后验分布。我们用玻尔兹曼分布对变形的似然性和弹性先验进行建模，并用马尔可夫链蒙特卡罗算法对后验进行表征。我们介绍了总结高维不确定性信息的方法，并展示了如何以有意义的方式可视化这些总结。基于一个临床神经外科数据集，我们证明了不确定性信息在神经外科决策中的重要性。
