Title: Algorithmic Implementation: An Introduction to a Low-Cost, GUI-Based, Semi-Unsupervised Microscopy Segmentation Framework Show Chinese title

Title: 算法实现：一种低成本、基于图形用户界面的半监督显微图像分割框架

Abstract: This article presents a novel microscopy image analysis framework designed for low-budget labs equipped with a standard CPU desktop. The Python-based program enables cytometric analysis of live, unstained cells in culture through an advanced computer vision and machine learning pipeline. Crucially, the framework operates on label-free data, requiring no manually annotated training data or training phase. It is accessible via a user-friendly, cross-platform GUI that requires no programming skills, while also providing a scripting interface for programmatic control and integration by developers. The end-to-end workflow performs semantic and instance segmentation, feature extraction, analysis, evaluation, and automated report generation. Its modular architecture supports easy maintenance and flexible integration while supporting both single-image and batch processing. Validated on several unstained cell types from the public dataset of livecells, the framework demonstrates superior accuracy and reproducibility compared to contemporary tools like Cellpose and StarDist. Its competitive segmentation speed on a CPU-based platform highlights its significant potential for basic research and clinical application-particularly in cell transplantation for personalised medicine and muscle regeneration therapies. The access to the application is available for reproducibility. Show Chinese abstract

Abstract: 本文介绍了一种新颖的显微图像分析框架，专为配备标准CPU台式机的低成本实验室设计。 基于Python的程序通过先进的计算机视觉和机器学习管道，实现了对培养中活体未染色细胞的细胞计数分析。 至关重要的是，该框架在无需手动标注训练数据或训练阶段的情况下，能够处理无标签数据。 它通过一个用户友好的跨平台图形界面进行访问，无需编程技能，同时为开发者提供了脚本接口以便程序控制和集成。 端到端的工作流程执行语义和实例分割、特征提取、分析、评估和自动化报告生成。 其模块化架构支持易于维护和灵活集成，同时支持单张图像和批量处理。 在公共数据集中的多个未染色细胞类型上进行了验证，与当前工具如Cellpose和StarDist相比，该框架表现出更高的准确性和可重复性。 在基于CPU的平台上，其具有竞争力的分割速度突显了其在基础研究和临床应用中的巨大潜力，尤其是在个性化医学中的细胞移植和肌肉再生治疗中。 为了便于复现，该应用程序的访问权限已开放。