无需标注数据，用快速高斯过程实现无监督细胞分割。
Unsupervised cell segmentation by fast Gaussian Processes
- 基于快速高斯过程构建无监督分割算法，免参数调优。
- 在真实和模拟数据中均实现高精度，优于现有方法。
- 适合处理亮度不均、形状多变的显微图像，尤其适用于新类型细胞。
细胞边界信息对分析时间序列显微视频中的细胞行为至关重要。现有监督分割工具（如ImageJ）需手动调参，且对物体形状有严格假设。尽管基于卷积神经网络的最新方法提升了精度，但依赖高质量标注图像，难以泛化到数据库外的新类型对象。本文提出一种基于快速高斯过程的新型无监督细胞分割算法，适用于噪声显微图像，无需参数调优或对物体形状做限制性假设。我们推导出适应不同亮度区域的鲁棒阈值判定标准，以区分物体与背景，并采用分水岭分割法分离重叠细胞。模拟研究与大规模真实显微图像分析均表明，该方法在可扩展性和准确性上显著优于现有方案。
原文摘要 · Abstract (English)
Cell boundary information is crucial for analyzing cell behaviors from time-lapse microscopy videos. Existing supervised cell segmentation tools, such as ImageJ, require tuning various parameters and rely on restrictive assumptions about the shape of the objects. While recent supervised segmentation tools based on convolutional neural networks enhance accuracy, they depend on high-quality labeled images, making them unsuitable for segmenting new types of objects not in the database. We developed a novel unsupervised cell segmentation algorithm based on fast Gaussian processes for noisy microscopy images without the need for parameter tuning or restrictive assumptions about the shape of the object. We derived robust thresholding criteria adaptive for heterogeneous images containing distinct brightness at different parts to separate objects from the background, and employed watershed segmentation to distinguish touching cell objects. Both simulated studies and real-data analysis of large microscopy images demonstrate the scalability and accuracy of our approach compared with the alternatives.
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