将CellProfiler的图像特征提取能力封装为可编程工具，提升生物图像分析自动化水平。
cp_measure: API-first feature extraction for image-based profiling workflows
- 基于API设计，将CellProfiler核心功能模块化，支持程序调用。
- 提取特征与原工具高度一致，且能无缝接入科学计算生态。
- 适用于需要大规模、可复现的细胞图像分析场景，如药物筛选与疾病研究。
生物图像分析传统上聚焦于特定视觉特征的测量。近年来，图像表型分析（image-based profiling）逐渐兴起，通过量化多种视觉特征形成全面的细胞状态图谱，揭示细胞状态、药物反应和疾病机制中的隐藏模式。尽管现有工具如CellProfiler可生成这些特征集，但其在自动化和可复现性方面存在障碍，阻碍了机器学习流程。本文提出cp_measure，一个将CellProfiler核心测量能力提取为模块化、API优先的Python库，实现程序化特征提取。实验表明，cp_measure提取的特征与CellProfiler保持高保真度，并能顺畅集成至科学计算生态系统。通过3D星形胶质细胞成像和空间转录组学应用，展示了该工具在构建可复现、自动化的图像表型分析流水线方面的有效性，适合大规模机器学习在计算生物学中的应用。
原文摘要 · Abstract (English)
Biological image analysis has traditionally focused on measuring specific visual properties of interest for cells or other entities. A complementary paradigm gaining increasing traction is image-based profiling - quantifying many distinct visual features to form comprehensive profiles which may reveal hidden patterns in cellular states, drug responses, and disease mechanisms. While current tools like CellProfiler can generate these feature sets, they pose significant barriers to automated and reproducible analyses, hindering machine learning workflows. Here we introduce cp_measure, a Python library that extracts CellProfiler's core measurement capabilities into a modular, API-first tool designed for programmatic feature extraction. We demonstrate that cp_measure features retain high fidelity with CellProfiler features while enabling seamless integration with the scientific Python ecosystem. Through applications to 3D astrocyte imaging and spatial transcriptomics, we showcase how cp_measure enables reproducible, automated image-based profiling pipelines that scale effectively for machine learning applications in computational biology.
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