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.

翻译：在这项工作中,我们使用了深神经网络培训程序之前和期间的表层前科。我们比较了这两种方法的结果,对各种准确度指标和与表层正确性直接相关的贝蒂数字错误进行了简单分解,并发现将表层信息纳入古典UNet模型的效果要好得多。我们在ISBI EM分解数据集上进行了实验。