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.

翻译：细胞边界信息对于从延时显微视频中分析细胞行为至关重要。现有的监督式细胞分割工具（如ImageJ）需要调整多种参数，并依赖于对物体形状的限制性假设。尽管近期基于卷积神经网络的监督式分割工具提升了准确性，但它们依赖于高质量的标注图像，因此不适用于分割数据库中未包含的新型物体。我们开发了一种基于快速高斯过程的新型非监督细胞分割算法，适用于噪声显微图像，无需参数调整或对物体形状的限制性假设。我们推导了适用于异质性图像的自适应鲁棒阈值标准（此类图像的不同区域具有显著亮度差异），以将物体与背景分离，并采用分水岭分割技术来区分接触的细胞物体。模拟研究和大尺度显微图像的实际数据分析均表明，与现有方法相比，我们的方法具有可扩展性和准确性。