Zhu Yaoyao, Huang Xiaolei, Wang Wei, Lopresti Daniel, Long Rodney, Antani Sameer, Xue Zhiyun, Thoma George
Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA.
J Signal Process Syst. 2008 May 28;55(1-3):185-207. doi: 10.1007/s11265-008-0215-5.
Comparison of a group of multiple observer segmentations is known to be a challenging problem. A good segmentation evaluation method would allow different segmentations not only to be compared, but to be combined to generate a "true" segmentation with higher consensus. Numerous multi-observer segmentation evaluation approaches have been proposed in the literature, and STAPLE in particular probabilistically estimates the true segmentation by optimal combination of observed segmentations and a prior model of the truth. An Expectation-Maximization (EM) algorithm, STAPLE'S convergence to the desired local minima depends on good initializations for the truth prior and the observer-performance prior. However, accurate modeling of the initial truth prior is nontrivial. Moreover, among the two priors, the truth prior always dominates so that in certain scenarios when meaningful observer-performance priors are available, STAPLE can not take advantage of that information. In this paper, we propose a Bayesian decision formulation of the problem that permits the two types of prior knowledge to be integrated in a complementary manner in four cases with differing application purposes: (1) with known truth prior; (2) with observer prior; (3) with neither truth prior nor observer prior; and (4) with both truth prior and observer prior. The third and fourth cases are not discussed (or effectively ignored) by STAPLE, and in our research we propose a new method to combine multiple-observer segmentations based on the maximum a posterior (MAP) principle, which respects the observer prior regardless of the availability of the truth prior. Based on the four scenarios, we have developed a web-based software application that implements the flexible segmentation evaluation framework for digitized uterine cervix images. Experiment results show that our framework has flexibility in effectively integrating different priors for multi-observer segmentation evaluation and it also generates results comparing favorably to those by the STAPLE algorithm and the Majority Vote Rule.
已知对一组多个观察者的分割结果进行比较是一个具有挑战性的问题。一种良好的分割评估方法不仅应能对不同的分割结果进行比较，还应能将它们组合起来以生成具有更高一致性的“真实”分割结果。文献中已经提出了许多多观察者分割评估方法，特别是STAPLE通过观察到的分割结果与真实情况的先验模型的最优组合，以概率方式估计真实分割。期望最大化（EM）算法是STAPLE用于收敛到期望的局部最小值的方法，它依赖于对真实情况先验和观察者性能先验的良好初始化。然而，对初始真实情况先验进行准确建模并非易事。此外，在这两个先验中，真实情况先验总是占主导地位，因此在某些情况下，当有意义的观察者性能先验可用时，STAPLE无法利用该信息。在本文中，我们提出了该问题的贝叶斯决策公式，它允许在四种具有不同应用目的的情况下以互补方式整合这两种先验知识：（1）已知真实情况先验；（2）有观察者先验；（3）既无真实情况先验也无观察者先验；（4）既有真实情况先验又有观察者先验。STAPLE未讨论（或实际上忽略了）第三和第四种情况，在我们的研究中，我们提出了一种基于最大后验（MAP）原则的新方法来组合多个观察者的分割结果，该方法无论真实情况先验是否可用，都尊重观察者先验。基于这四种情况，我们开发了一个基于网络的软件应用程序，该程序为数字化子宫颈图像实现了灵活的分割评估框架。实验结果表明，我们的框架在有效整合不同先验用于多观察者分割评估方面具有灵活性，并且其生成的结果与STAPLE算法和多数投票规则生成的结果相比更具优势。
