Title: Intelligent Software System for Low-Cost, Brightfield Segmentation: Algorithmic Implementation for Cytometric Auto-Analysis Show Chinese title

Title: 低成本明场分割智能软件系统：细胞计数自动分析的算法实现

Abstract: Bright-field microscopy, a cost-effective solution for live-cell culture, is often the only resource available, along with standard CPUs, for many low-budget labs. The inherent chal- lenges of bright-field images - their noisiness, low contrast, and dynamic morphology - coupled with a lack of GPU resources and complex software interfaces, hinder the desired research output. This article presents a novel microscopy image analysis frame- work 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 archi- tecture 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 applications - particularly in cell transplantation for personalised medicine and muscle regeneration therapies. The access to the application is available for reproducibility Show Chinese abstract

Abstract: 明场显微镜是一种经济有效的活细胞培养解决方案，对于许多预算有限的实验室来说，它常常是唯一可用的资源，同时还配有标准的CPU。 明场图像固有的挑战——它们的噪声、低对比度和动态形态——加上缺乏GPU资源和复杂的软件界面，阻碍了期望的研究成果。 本文介绍了一种专为配备标准CPU台式机的预算有限实验室设计的新型显微镜图像分析框架。 基于Python的程序通过先进的计算机视觉和机器学习管道，实现了对培养中活的未染色细胞的细胞计数分析。 至关重要的是，该框架在无标签数据上运行，不需要手动标注的训练数据或训练阶段。 它可通过一个用户友好的跨平台GUI访问，无需编程技能，同时为开发者提供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，同时支持单图和批量处理。 在公共数据集中的几种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，其竞争性的分割速度突显了其在基础研究和临床应用中的显著潜力——特别是在个性化医学中的细胞移植和肌肉再生治疗中。 应用程序的访问权限可用于重现性