Abstract
The most popular networks used for cell segmentation (e.g. Cellpose, Stardist, HoverNet,...) rely on a prediction of a distance map. It yields unprecedented accuracy but hinges on fully annotated datasets. This is a serious limitation to generate training sets and perform transfer learning. In this paper, we propose a method that still relies on the distance map and handles partially annotated objects. We evaluate the performance of the proposed approach in the contexts of frugal learning, transfer learning and regular learning on regular databases. Our experiments show that it can lead to substantial savings in time and resources without sacrificing segmentation quality. The proposed algorithm is embedded in a user-friendly Napari plugin.
Abstract (translated)
用于细胞分割的最流行的网络（例如 Cellpose、Stardist、HoverNet 等）依赖于距离图预测。这种方法实现了前所未有的准确性，但需要完全注释的数据集。这在生成训练集和进行迁移学习方面是一个严重的限制。在这篇论文中，我们提出了一种方法，该方法仍然基于距离图，并能够处理部分标注的对象。我们在节俭学习、迁移学习和常规数据库上的常规学习环境中评估了所提方法的性能。实验结果显示，在不牺牲分割质量的情况下，它可以在时间和资源上实现显著节省。提出的算法被嵌入到一个用户友好的 Napari 插件中。
URL
https://arxiv.org/abs/2508.17798
