PySlyde：一个轻量级、开源的病理学预处理工具包
PySlyde: A Lightweight, Open-Source Toolkit for Pathology Preprocessing
AI总结：
PySlyde是一个基于OpenSlide的轻量级开源Python工具包，通过统一组织检测、切块、染色归一化和注释解析等预处理流程，简化WSI预处理并提升可重复性，加速AI就绪数据集生成。
摘要：
人工智能（AI）与病理学的融合正在通过改进诊断、治疗规划和患者预后推动精准医学的发展。数字化全切片图像（WSIs）捕获了对理解疾病生物学至关重要的丰富空间和形态学信息，然而其十亿像素级的规模和变异性给标准化和分析带来了重大挑战。稳健的预处理，涵盖组织检测、切片分割、染色归一化和注释解析，至关重要，但往往受到碎片化且不一致的工作流程的限制。我们提出了PySlyde，一个基于OpenSlide构建的轻量级、开源Python工具包，旨在简化和标准化WSI预处理。PySlyde提供了直观的API，用于切片加载、注释管理、组织检测、切块和特征提取，并与现代病理学基础模型兼容。通过统一这些流程，它简化了WSI预处理，增强了可重复性，并加速了AI就绪数据集的生成，使研究人员能够专注于模型开发和下游分析。
英文摘要：
The integration of artificial intelligence (AI) into pathology is advancing precision medicine by improving diagnosis, treatment planning, and patient outcomes. Digitised whole-slide images (WSIs) capture rich spatial and morphological information vital for understanding disease biology, yet their gigapixel scale and variability pose major challenges for standardisation and analysis. Robust preprocessing, covering tissue detection, tessellation, stain normalisation, and annotation parsing is critical but often limited by fragmented and inconsistent workflows. We present PySlyde, a lightweight, open-source Python toolkit built on OpenSlide to simplify and standardise WSI preprocessing. PySlyde provides an intuitive API for slide loading, annotation management, tissue detection, tiling, and feature extraction, compatible with modern pathology foundation models. By unifying these processes, it streamlines WSI preprocessing, enhances reproducibility, and accelerates the generation of AI-ready datasets, enabling researchers to focus on model development and downstream analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
