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, a 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至关重要，例如神经元电子显微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偏移。为了提升分割结果的拓扑精度，我们提出了Skea-Topo Aware损失函数，这是一种新颖的损失函数，能够综合考虑每个目标的形状以及像素的拓扑重要性。该损失函数由两部分组成：首先，骨架感知加权损失通过利用骨架更好地建模目标几何形状，从而提升分割精度；其次，边界修正项同时利用真实值与预测结果中的前景和背景骨架，有效识别并强调预测误差中拓扑关键像素。实验证明，在三个不同的边界分割数据集上，基于客观和主观评估，我们的方法相较于13种最先进方法，在VI指标上拓扑一致性最高提升7个百分点。代码已开源在https://github.com/clovermini/Skea_topo。