Seg-metrics: a Python package to compute segmentation metrics

In the last decade, the research of artificial intelligence on medical images has attracted researchers' interest. One of the most popular directions is automated medical image segmentation (MIS) using deep learning, which aims to automatically assign labels to pixels so that the pixels with the same label from a segmented object. However, in the past years a strong trend of highlighting or cherry-picking improper metrics to show particularly high scores close to 100% was revealed in scientific publishing of MIS studies [1]. In addition, even though there are some papers that evaluate image segmentation results from different perspectives, the implementation of their evaluation algorithms is inconsistent. This is due to the lack of a universal metric library in Python for standardized and reproducible evaluation. Therefore, we propose to develop an open-source publicly available Python package seg-metrics, which aims to evaluate the performance of MIS models.