仅用一张图中几十个细胞标注，就能精准分割各类细胞。
CellSeg1: Robust Cell Segmentation with One Training Image
- 基于SAM的低秩适应，仅需少量标注即可训练
- 单图训练达0.81平均mAP，媲美数百张图训练模型
- 适合新细胞类型快速部署，极大降低标注成本
细胞分割研究正转向通用模型以应对多样化的细胞形态和成像模式。然而，面对不断出现的新细胞类型和成像技术，现有模型仍需数百甚至上千个标注细胞进行微调。我们提出CellSeg1，一种仅需在一张图像中提供几十个标注细胞即可实现任意形态与模态细胞分割的实用方案。通过采用低秩适配的Segment Anything Model（SAM），实现了鲁棒的细胞分割。在19个不同细胞数据集上测试，仅用一张图像训练的CellSeg1在0.5 IoU下达到0.81的平均mAP，表现接近需超500张图像训练的现有模型。在TissueNet跨数据集测试中也展现出优异泛化能力。研究发现，对数十个密集排列、大小多样的细胞进行高质量标注是有效分割的关键。CellSeg1为细胞分割提供了极低标注成本的高效解决方案。
原文摘要 · Abstract (English)
Recent trends in cell segmentation have shifted towards universal models to handle diverse cell morphologies and imaging modalities. However, for continuously emerging cell types and imaging techniques, these models still require hundreds or thousands of annotated cells for fine-tuning. We introduce CellSeg1, a practical solution for segmenting cells of arbitrary morphology and modality with a few dozen cell annotations in 1 image. By adopting Low-Rank Adaptation of the Segment Anything Model (SAM), we achieve robust cell segmentation. Tested on 19 diverse cell datasets, CellSeg1 trained on 1 image achieved 0.81 average mAP at 0.5 IoU, performing comparably to existing models trained on over 500 images. It also demonstrated superior generalization in cross-dataset tests on TissueNet. We found that high-quality annotation of a few dozen densely packed cells of varied sizes is key to effective segmentation. CellSeg1 provides an efficient solution for cell segmentation with minimal annotation effort.
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