Elhabian Shireen Y, Agrawal Praful, Whitaker Ross T
Scientific Computing and Imaging Institute, University of Utah, USA.
Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:660-663. doi: 10.1109/ISBI.2016.7493353. Epub 2016 Jun 16.
Probabilistic label maps are a useful tool for important medical image analysis tasks such as segmentation, shape analysis, and atlas building. Existing methods typically rely on blurred signed distance maps or smoothed label maps to model uncertainties and shape variabilities, which do not conform to any generative model or estimation process, and are therefore suboptimal. In this paper, we propose to learn probabilistic label maps using a generative model on given set of binary label maps. The proposed approach generalizes well on unseen data while simultaneously capturing the variability in the training samples. Efficiency of the proposed approach is demonstrated for consensus generation and shape-based clustering using synthetic datasets as well as left atrial segmentations from late-gadolinium enhancement MRI.
概率标签图是用于诸如分割、形状分析和图谱构建等重要医学图像分析任务的有用工具。现有方法通常依赖模糊符号距离图或平滑标签图来对不确定性和形状变异性进行建模，这些方法不符合任何生成模型或估计过程，因此是次优的。在本文中，我们建议在给定的二元标签图集上使用生成模型来学习概率标签图。所提出的方法在未见数据上具有良好的泛化能力，同时能够捕捉训练样本中的变异性。使用合成数据集以及延迟钆增强MRI的左心房分割，证明了所提出方法在一致性生成和基于形状的聚类方面的有效性。
