论文标题
基于学习的基于学习的框架，用于使用准文化映射
A Learning-based Framework for Topology-Preserving Segmentation using Quasiconformal Mappings
论文作者
论文摘要
我们建议使用拓扑的分割网络，这是一个基于变形的模型，可以在保持图像中的对象，同时保持其拓扑特性。该网络生成的分割掩模，即使使用有限的数据训练，也具有与模板蒙版相同的拓扑。该网络由两个组件组成：变形估计网络，该网络产生了变形图，该图形映射扭曲模板掩模以封闭了感兴趣的区域，而Beltrami调整模块则通过基于Quasiconformformal theorories的Quasiciconformal Aresories截断了相关的Beltrami系数，从而确保了变形图的射击图。所提出的网络也可以以无监督的方式进行培训，从而消除了对培训数据的需求。这是通过纳入无监督的分割损失来实现的。我们在各种图像数据集上的实验结果表明，TPSN比具有正确拓扑的最先进模型实现了更好的分割精度。此外，我们演示了TPSN处理多个对象分割的能力。
We propose the Topology-Preserving Segmentation Network, a deformation-based model that can extract objects in an image while maintaining their topological properties. This network generates segmentation masks that have the same topology as the template mask, even when trained with limited data. The network consists of two components: the Deformation Estimation Network, which produces a deformation map that warps the template mask to enclose the region of interest, and the Beltrami Adjustment Module, which ensures the bijectivity of the deformation map by truncating the associated Beltrami coefficient based on Quasiconformal theories. The proposed network can also be trained in an unsupervised manner, eliminating the need for labeled training data. This is achieved by incorporating an unsupervised segmentation loss. Our experimental results on various image datasets show that TPSN achieves better segmentation accuracy than state-of-the-art models with correct topology. Furthermore, we demonstrate TPSN's ability to handle multiple object segmentation.
