无需标注数据与GPU，用CPU即可实现活细胞高精度自动分析
Intelligent Software System for Low-Cost, Brightfield Segmentation: Algorithmic Implementation for Cytometric Auto-Analysis
- 基于无监督视觉与机器学习的端到端分析框架
- 在多个细胞数据集上优于Cellpose和StarDist，准确率更高
- 适合无编程基础者使用，也支持开发者集成
明场显微镜是许多低预算实验室唯一可用的活细胞成像工具，但其图像噪声大、对比度低、形态多变，且缺乏GPU资源与易用软件，严重限制研究效率。本文提出一种专为标准CPU桌面设计的开源图像分析框架，基于Python实现，可对未染色活细胞进行全自动细胞计数与分析。该框架无需人工标注数据或训练过程，通过先进的计算机视觉与机器学习流程完成语义与实例分割、特征提取、分析评估及自动生成报告。系统具备跨平台图形界面，无需编程知识即可操作，同时提供脚本接口供开发者集成。模块化架构支持单图与批量处理，已在公开数据集LiveCells上的多种未染色细胞类型中验证，表现优于当前主流工具如Cellpose与StarDist。在仅用CPU的条件下仍保持高效处理速度，展现出在基础研究与个性化医疗（如细胞移植、肌肉再生）中的巨大应用潜力。代码与应用已公开，保障可复现性。
原文摘要 · Abstract (English)
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 challenges 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 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 applications -- particularly in cell transplantation for personalised medicine and muscle regeneration therapies. The access to the application is available for reproducibility
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