Abstract
While advances in machine learning have enabled automated cell segmentation, users often face challenges in parameter tuning until reaching their desired results. To address this issue, we developed PomSeg, a membrane segmentation method based on persistent homology. Since persistent homology captures topological features of input data, PomSeg parameters reflect cell shape information, enabling intuitive and efficient parameter tuning. This adaptivity, together with stability for noise of persistent homology, enables robust application of PomSeg to various image types, including coarse-resolution data. By applying PomSeg to early mouse embryo membrane images and other publicly available datasets, we demonstrated its flexibility, versatility, and robustness, along with agreement with ground truth. Additionally, we showed the potential of PomSeg extension by incorporating a machine learning tool in its process. These features make PomSeg a valuable tool for researchers pursuing control and interpretability in segmentation, as well as indicating wider applications beyond a machine learning alternative.