Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. The most popular CNN-based methods are optimised using pixel wise loss functions, ignorant of the spatially extended features that characterise anatomy. Therefore, whilst sharing a high spatial overlap with the ground truth, inferred CNN-based segmentations can lack coherence, including spurious connected components, holes and voids. Such results are implausible, violating anticipated anatomical topology. In response, (single-class) persistent homology-based loss functions have been proposed to capture global anatomical features. Our work extends these approaches to the task of multi-class segmentation. Building an enriched topological description of all class labels and class label pairs, our loss functions make predictable and statistically significant improvements in segmentation topology using a CNN-based post-processing framework. We also present (and make available) a highly efficient implementation based on cubical complexes and parallel execution, enabling practical application within high resolution 3D data for the first time. We demonstrate our approach on 2D short axis and 3D whole heart CMR segmentation, advancing a detailed and faithful analysis of performance on two publicly available datasets.
翻译:以CNN为基础的最流行的方法是使用像素智慧损失功能优化使用最受欢迎的方法。我们的工作将这些方法扩大到多级分类分类任务。我们的工作将这些方法扩大到多级分类分类任务。我们对所有类标签和类标签配对的丰富地貌描述,我们的损失功能利用CNN的后处理框架,在分解表层表层学上作出可预见和统计上的重大改进。我们还提出(和提供)基于立体复合和平行执行的高效执行,首次在高分辨率3D数据中进行精确应用。我们展示了2D的连续直径和连续直径分析。