用部分标注数据实现高精度细胞分割，节省大量标注成本。
Sketchpose: Learning to Segment Cells with Partial Annotations
- 基于距离图预测，支持部分标注的细胞分割
- 在多种学习场景下保持高精度，减少标注时间与资源消耗
- 集成于Napari插件，操作简单适合生物图像研究者
目前主流的细胞分割网络（如Cellpose、Stardist、HoverNet等）依赖距离图预测，虽精度极高，但需完全标注数据集，严重制约训练集构建与迁移学习。本文提出一种新方法，在保留距离图机制的基础上，可处理部分标注样本。我们在少样本学习、迁移学习及常规学习场景下评估该方法，结果表明其可在不牺牲分割质量的前提下显著降低时间和资源投入。所提算法已嵌入用户友好的Napari插件中，便于实际应用。
原文摘要 · Abstract (English)
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.
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