论文标题
基于同源性的持续同源性拓扑损失函数，用于心脏MRI的多级CNN分割
A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI
论文作者
论文摘要
对于空间重叠，基于CNN的短轴心血管磁共振（CMR）图像的分割已达到与观察者间变化一致的性能水平。但是，常规的培训程序经常取决于像素损失功能，从而限制了相对于扩展或全局特征的优化。结果，被推断的分割可能缺乏空间连贯性，包括伪造的连接组件或孔。这种结果是不可信的，违反了图像段的预期拓扑，这通常是先验的。在应对这一挑战时，已发表的工作采用了持续的同源性，构建了拓扑损失功能，以评估图像段对明确的先验。通过考虑所有可能的标签和标签对来构建对分割拓扑的更丰富的描述，我们将这些损失扩展到多级分割的任务。这些拓扑先验使我们能够在ACDC短轴CMR训练数据集的150个示例中解决所有拓扑错误，而不会牺牲重叠性能。
With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation. However, conventional training procedures frequently depend on pixel-wise loss functions, limiting optimisation with respect to extended or global features. As a result, inferred segmentations can lack spatial coherence, including spurious connected components or holes. Such results are implausible, violating the anticipated topology of image segments, which is frequently known a priori. Addressing this challenge, published work has employed persistent homology, constructing topological loss functions for the evaluation of image segments against an explicit prior. Building a richer description of segmentation topology by considering all possible labels and label pairs, we extend these losses to the task of multi-class segmentation. These topological priors allow us to resolve all topological errors in a subset of 150 examples from the ACDC short axis CMR training data set, without sacrificing overlap performance.
