标题： 基于模糊逻辑和空间统计的半监督显微分割方法用于使用图形用户界面的跨领域分析 显示英文标题

标题： Semi-Unsupervised Microscopy Segmentation with Fuzzy Logic and Spatial Statistics for Cross-Domain Analysis Using a GUI

摘要： 明场显微镜下未染色的活细胞成像由于对比度低、形态动态、光照不均匀和缺乏标记而具有挑战性。 深度学习在染色的高对比度图像上取得了最先进的性能，但需要大量的标注数据、昂贵的硬件，并且在光照不均匀的情况下表现不佳。 本研究通过引入一次校准辅助的无监督框架，提出了一种低成本、轻量级、无需注释的分割方法，该框架适用于不同的成像模式和图像类型。 该框架通过局部均值的空间标准差确定背景。 不确定的像素通过模糊逻辑、节点强度的累积平方位移、统计特征进行解析，随后进行后分割去噪校准，该校准结果保存为配置文件以供重复使用，直到噪声模式或物体类型发生显著变化。 该程序可以作为脚本或图形界面运行，方便非程序员使用。 该方法使用\textit{交并比}、\textit{F1分数}和其他指标进行了严格评估，通过 Wilcoxon 符号秩检验确认了统计显著性。 在未染色的明场肌母细胞（C2C12）图像上，它优于\textit{Cellpose 3.0}和\textit{星距离}，将 IoU 提高了高达 48%（平均 IoU = 0.43，F1 = 0.60）。 在相差显微镜中，它在\textit{LIVECell}数据集（$n = 3178$）上实现了平均 IoU 为 0.69 和 F1 得分为 0.81，专家一致性（$\kappa > 0.75$）证实了跨模态的鲁棒性。 激光影响的聚合物表面的成功分割进一步证实了跨领域的鲁棒性。 通过引入\textit{同质图像平面}概念，这项工作为无训练、无标注的分割提供了新的理论基础。 该框架在 CPU 上运行效率高，避免了细胞染色，并适用于活细胞成像和生物医学应用。 显示英文摘要

摘要： Brightfield microscopy of unstained live cells is challenging due to low contrast, dynamic morphology, uneven illumination, and lack of labels. Deep learning achieved SOTA performance on stained, high-contrast images but needs large labeled datasets, expensive hardware, and fails under uneven illumination. This study presents a low-cost, lightweight, annotation-free segmentation method by introducing one-time calibration-assisted unsupervised framework adaptable across imaging modalities and image type. The framework determines background via spatial standard deviation from the local mean. Uncertain pixels are resolved using fuzzy logic, cumulative squared shift of nodal intensity, statistical features, followed by post-segmentation denoising calibration which is saved as a profile for reuse until noise pattern or object type substantially change. The program runs as a script or graphical interface for non-programmers. The method was rigorously evaluated using \textit{IoU}, \textit{F1-score}, and other metrics, with statistical significance confirmed via Wilcoxon signed-rank tests. On unstained brightfield myoblast (C2C12) images, it outperformed \textit{Cellpose 3.0} and \textit{StarDist}, improving IoU by up to 48\% (average IoU = 0.43, F1 = 0.60). In phase-contrast microscopy, it achieved a mean IoU of 0.69 and an F1-score of 0.81 on the \textit{LIVECell} dataset ($n = 3178$), with substantial expert agreement ($\kappa > 0.75$) confirming cross-modality robustness. Successful segmentation of laser-affected polymer surfaces further confirmed cross-domain robustness. By introducing the \textit{Homogeneous Image Plane} concept, this work provides a new theoretical foundation for training-free, annotation-free segmentation. The framework operates efficiently on CPU, avoids cell staining, and is practical for live-cell imaging and biomedical applications.