改进SAM模型，提升显微镜细胞分割精度
Revisiting foundation models for cell instance segmentation
- 提出自动提示生成策略，增强SAM系模型在显微图像表现
- μSAM+APG在多种显微数据集上超越现有模型，接近最优水平
- 适合从事生物图像分析、医学影像研究的科研人员参考
细胞分割是显微图像分析的基础任务。尽管已有多个针对细胞分割的奠基模型（如CellPoseSAM、CellSAM、μSAM）被提出，几乎全部基于段落任意模型（SAM）改进而来。近期，SAM2和SAM3进一步扩展了通用分割模型的能力。本文全面评估了细胞分割专用模型（CellPoseSAM、CellSAM、μSAM）与通用分割模型（SAM、SAM2、SAM3）在多类（光）显微图像数据集上的表现，涵盖细胞、细胞核及类器官分割任务。此外，我们提出一种新型实例分割策略——自动提示生成（APG），可显著提升以μSAM为基底的模型性能，其效果与当前最优模型CellPoseSAM相当。本工作还揭示了SAM类模型适配显微图像的关键经验，并为构建更强大的显微图像奠基模型提供路径。代码已开源：https://github.com/computational-cell-analytics/micro-sam。
原文摘要 · Abstract (English)
Cell segmentation is a fundamental task in microscopy image analysis. Several foundation models for cell segmentation have been introduced, virtually all of them are extensions of Segment Anything Model (SAM), improving it for microscopy data. Recently, SAM2 and SAM3 have been published, further improving and extending the capabilities of general-purpose segmentation foundation models. Here, we comprehensively evaluate foundation models for cell segmentation (CellPoseSAM, CellSAM, $μ$SAM) and for general-purpose segmentation (SAM, SAM2, SAM3) on a diverse set of (light) microscopy datasets, for tasks including cell, nucleus and organoid segmentation. Furthermore, we introduce a new instance segmentation strategy called automatic prompt generation (APG) that can be used to further improve SAM-based microscopy foundation models. APG consistently improves segmentation results for $μ$SAM, which is used as the base model, and is competitive with the state-of-the-art model CellPoseSAM. Moreover, our work provides important lessons for adaptation strategies of SAM-style models to microscopy and provides a strategy for creating even more powerful microscopy foundation models. Our code is publicly available at https://github.com/computational-cell-analytics/micro-sam.
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