Asman Andrew J, Landman Bennett A
Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):426-34. doi: 10.1007/978-3-642-33454-2_53.
Multi-atlas segmentation provides a general purpose, fully automated class of techniques for transferring spatial information from an existing dataset ("atlases") to a previously unseen context ("target") through image registration. The method used to combine information after registration ("label fusion") has a substantial impact on the overall accuracy and robustness. In practice, weighted voting techniques have dramatically outperformed algorithms based on statistical fusion (i.e., algorithms that incorporate rater performance into the estimation process--STAPLE). We posit that a critical limitation of statistical techniques (as generally proposed) is that they fail to incorporate intensity seamlessly into the estimation process and models of observation error. Herein, we propose a novel statistical fusion algorithm, non-local STAPLE, which merges the STAPLE framework with a non-local means perspective. Non-local STAPLE (1) seamlessly integrates intensity into the estimation process, (2) provides a theoretically consistent model of multi-atlas observation error, and (3) largely bypasses the need for group-wise unbiased registrations. We demonstrate significant improvements in two empirical multi-atlas experiments.
多图谱分割提供了一类通用的、完全自动化的技术，用于通过图像配准将空间信息从现有数据集（“图谱”）传递到之前未见过的情境（“目标”）。配准后用于组合信息的方法（“标签融合”）对整体准确性和鲁棒性有重大影响。在实践中，加权投票技术的表现显著优于基于统计融合的算法（即，将评分者表现纳入估计过程的算法——STAPLE）。我们认为，统计技术（如通常所提出的）的一个关键局限性在于它们未能将强度无缝纳入估计过程和观测误差模型。在此，我们提出一种新颖的统计融合算法——非局部STAPLE，它将STAPLE框架与非局部均值视角相结合。非局部STAPLE（1）将强度无缝整合到估计过程中，（2）提供了一个理论上一致的多图谱观测误差模型，并且（3）在很大程度上无需进行组间无偏配准。我们在两个实证多图谱实验中展示了显著的改进。
