PySlyde简化病理切片预处理，让研究者更快构建AI可用数据集。
PySlyde: A Lightweight, Open-Source Toolkit for Pathology Preprocessing
- 基于OpenSlide构建，统一切片加载、组织检测、分块等流程
- 支持注释解析与染色归一化，提升数据标准化程度
- 轻量开源，适配现代病理大模型，加速研发进程
人工智能在病理学中的应用正推动精准医疗发展，提升诊断、治疗规划和患者预后。数字化全幻灯片图像（WSI）包含丰富的空间与形态信息，对理解疾病生物学至关重要，但其千兆像素级规模和多样性给标准化与分析带来挑战。可靠的预处理流程——包括组织检测、分块、染色归一化和注释解析——至关重要，却常受限于碎片化且不一致的工作流。我们提出PySlyde，一个基于OpenSlide的轻量级、开源Python工具包，旨在简化并标准化WSI预处理。PySlyde提供直观API，支持切片加载、注释管理、组织检测、分块及特征提取，兼容现代病理基础模型。通过整合这些步骤，它提升了预处理效率与可复现性，加速生成适合AI使用的数据集，使研究者能聚焦于模型开发与下游分析。
原文摘要 · Abstract (English)
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.
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