用仿真数据微调SAM，实现荧光显微镜下线粒体精准分割
SAM for Robust Mitochondria Instance Segmentation in Fluorescence Microscopy
- 通过模拟荧光显微镜光学特性生成合成数据，解决标注数据稀缺问题
- 在真实数据上平均Dice得分提升，精度优于现有基线模型
- 适合需要高精度线粒体分割的生物医学图像分析研究者
荧光显微镜中线粒体的形态分析对理解细胞健康、能量代谢和调控至关重要。尽管基础模型如分割一切模型（SAM）已革新自然图像分割，但其直接应用于荧光显微镜仍受显著领域偏移限制，表现为衍射极限分辨率、低对比度及复杂重叠的细胞器网络。此外，稳健模型的发展受限于高质量手动标注的线粒体实例分割数据集严重不足。本文提出一种可扩展解决方案：仅在合成生成的荧光显微镜数据上微调SAM。我们模拟真实线粒体数据并再现荧光显微镜的光学特性，构建大规模标注数据集。在经筛选的真实手动标注荧光显微镜图像数据集上评估微调模型。定性与定量分析表明，该合成数据微调模型在精度和平均Dice分数上均优于强基线模型。本工作验证了仿真辅助训练在荧光显微镜实例分割中的潜力。
原文摘要 · Abstract (English)
The morphological analysis of mitochondria in fluorescence microscopy (FM) is crucial for understanding cellular health, energy production, and metabolic regulation. While foundation models like the Segment Anything Model (SAM) have revolutionized natural image segmentation, their direct application to FM is hindered by a significant domain shift characterized by diffraction-limited resolution, low contrast, and complex overlapping organelle networks. Furthermore, the development of robust models is bottlenecked by a severe lack of high-quality, manually annotated instance segmentation datasets for mitochondria. In this paper, we propose a scalable solution to this data scarcity by finetuning SAM exclusively on synthetically generated FM data. We simulate realistic mitochondria data and emulate the optical properties of fluorescence microscopes to create a large-scale annotated dataset. We evaluate our fine-tuned model on a curated dataset of real, manually annotated FM images. Qualitative and quantitative analyses demonstrate that our synthetically fine-tuned model improves precision and average dice score over strong baselines. This work establishes the potential of simulation-assisted training for FM instance segmentation.
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