Iglesias Juan Eugenio, Sabuncu Mert Rory, Van Leemput Koen
Martinos Center for Biomedical Imaging, MGH, Harvard Medical School, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):50-7. doi: 10.1007/978-3-642-33454-2_7.
Many successful segmentation algorithms are based on Bayesian models in which prior anatomical knowledge is combined with the available image information. However, these methods typically have many free parameters that are estimated to obtain point estimates only, whereas a faithful Bayesian analysis would also consider all possible alternate values these parameters may take. In this paper, we propose to incorporate the uncertainty of the free parameters in Bayesian segmentation models more accurately by using Monte Carlo sampling. We demonstrate our technique by sampling atlas warps in a recent method for hippocampal subfield segmentation, and show a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the method also yields informative "error bars" on the segmentation results for each of the individual sub-structures.
许多成功的分割算法都基于贝叶斯模型，其中先验解剖学知识与可用图像信息相结合。然而，这些方法通常有许多自由参数，这些参数仅被估计以获得点估计值，而忠实的贝叶斯分析还会考虑这些参数可能取的所有可能的替代值。在本文中，我们建议通过使用蒙特卡罗采样更准确地将自由参数的不确定性纳入贝叶斯分割模型。我们通过在最近的海马亚区分割方法中对图谱变形进行采样来展示我们的技术，并在阿尔茨海默病分类任务中显示出显著的改进。作为额外的好处，该方法还为每个单独的子结构的分割结果生成信息丰富的“误差条”。
