论文标题
拓扑先验的图像分割
Image Segmentation with Topological Priors
论文作者
论文摘要
用拓扑先验解决细分任务被证明在细尺度结构中犯了更少的错误。在这项工作中，我们在深度神经网络培训程序之前和期间都使用拓扑先验。我们将两种方法的结果与各种精确度指标和Betti数字误差的简单分割进行了比较，这与拓扑正确性直接相关，并发现将拓扑信息纳入经典的UNET模型中的性能要好得多。我们在ISBI EM分割数据集上进行了实验。
Solving segmentation tasks with topological priors proved to make fewer errors in fine-scale structures. In this work, we use topological priors both before and during the deep neural network training procedure. We compared the results of the two approaches with simple segmentation on various accuracy metrics and the Betti number error, which is directly related to topological correctness, and discovered that incorporating topological information into the classical UNet model performed significantly better. We conducted experiments on the ISBI EM segmentation dataset.
