Topological consistency plays a crucial role in the task of boundary segmentation for reticular images, such as cell membrane segmentation in neuron electron microscopic images, grain boundary segmentation in material microscopic images and road segmentation in aerial images. In these fields, topological changes in segmentation results have a serious impact on the downstream tasks, which can even exceed the misalignment of the boundary itself. To enhance the topology accuracy in segmentation results, we propose the Skea-Topo Aware loss, which is a novel loss function that takes into account the shape of each object and topological significance of the pixels. It consists of two components. First, the skeleton-aware weighted loss improves the segmentation accuracy by better modeling the object geometry with skeletons. Second, a boundary rectified term effectively identifies and emphasizes topological critical pixels in the prediction errors using both foreground and background skeletons in the ground truth and predictions. Experiments prove that our method improves topological consistency by up to 7 points in VI compared to 13 state-of-art methods, based on objective and subjective assessments across three different boundary segmentation datasets. The code is available at https://github.com/clovermini/Skea_topo.

翻译：拓扑一致性在网状图像的边界分割任务中起着至关重要的作用，例如神经电子显微镜图像中的细胞膜分割、材料显微图像中的晶界分割以及航拍图像中的道路分割。在这些领域中，分割结果中的拓扑变化对下游任务产生严重影响，其影响甚至可能超过边界本身的对齐误差。为提升分割结果的拓扑精度，我们提出了Skea-Topo Aware损失函数，这是一种新的损失函数，能兼顾每个物体的形状和像素的拓扑显著性。该损失函数包含两个部分：首先，基于骨架感知的加权损失通过利用骨架更好地建模物体几何形状，从而提高分割精度；其次，边界修正项通过使用真实标注和预测结果中的前景与背景骨架，有效识别并强调预测误差中拓扑关键像素。实验证明，在三个不同边界分割数据集上，基于客观和主观评估，与13种最先进方法相比，本文方法在VI指标上将拓扑一致性提升了多达7个百分点。代码已开源至https://github.com/clovermini/Skea_topo。