Image segmentation is an important subtask in biomedical research applications, such as estimating the position and shape of a tumor. Unfortunately, advanced image segmentation methods are not widely applied in research applications as they often miss features, such as uncertainty communication, and may lack an intuitive approach for the use of the underlying algorithm. To solve this problem, this paper fuses a fuzzy and a hierarchical segmentation approach together, thus providing a flexible multiclass segmentation method based on probabilistic path propagations. By utilizing this method, analysts and physicians can map their mental model of image components and their composition to higher level objects. The probabilistic segmentation of higher order components is propagated along the user-defined hierarchy to highlight the potential of improvement resulting in each level of hierarchy by providing an intuitive representation. The effectiveness of this approach is demonstrated by evaluating our segmentations of biomedical datasets, comparing it to the state-of-the-art segmentation approaches, and an extensive user study.

中文翻译：

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使用概率路径传播进行生物医学研究的分层图像语义

图像分割是生物医学研究应用中的一个重要子任务，例如估计肿瘤的位置和形状。不幸的是，先进的图像分割方法在研究应用中并未得到广泛应用，因为它们经常遗漏诸如不确定性通信等特征，并且可能缺乏使用底层算法的直观方法。为了解决这个问题，本文将模糊和分层分割方法融合在一起，从而提供了一种基于概率路径传播的灵活的多类分割方法。通过使用这种方法，分析师和医生可以将他们的图像组件及其组成的心理模型映射到更高级别的对象。高阶组件的概率分割沿着用户定义的层次结构传播，以通过提供直观的表示来突出导致每个层次结构级别的改进潜力。通过评估我们对生物医学数据集的分割，将其与最先进的分割方法进行比较，以及广泛的用户研究，证明了这种方法的有效性。