从传统到深度学习的全切片图像配准方法：一项方法论综述
From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review
AI总结：
本综述系统梳理了全切片图像配准从传统到深度学习的方法，分析其挑战、数据集、工具及未来趋势，旨在填补该领域综述空白。
摘要：
全切片图像（WSI）配准是组织病理学中分析肿瘤微环境（TME）的一项基本任务。它涉及将同一组织切片的连续切片或同一组织样本的连续切片之间的空间信息进行对齐。组织切片在成像前通常使用单一或多个生物标志物进行染色，其目标是识别沿Z轴相邻的细胞核，以创建三维图像或识别TME中的细胞亚类。与放射学图像配准（如磁共振成像或计算机断层扫描）相比，该任务因多种因素而更具挑战性。这些因素包括图像具有千兆像素级别的大小、不同染色组织之间外观的差异、非连续切片之间结构和形态的变化，以及伪影、撕裂和变形的存在。目前，文献中关于现有方法及其局限性、以及它们所带来的挑战和机遇的综述存在明显空白。我们旨在提供对现有方法及其在不同目的下应用的全面理解。此外，我们调查了当前用于WSI配准的深度学习方法，强调其多样化的方法论。我们考察了可用的数据集，并探索了该领域使用的工具和软件。最后，我们指出了该研究领域中的开放挑战和潜在的未来趋势。
英文摘要：
Whole slide image (WSI) registration is an essential task for analysing the tumour microenvironment (TME) in histopathology. It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample. The tissue sections are usually stained with single or multiple biomarkers before imaging, and the goal is to identify neighbouring nuclei along the Z-axis for creating a 3D image or identifying subclasses of cells in the TME. This task is considerably more challenging compared to radiology image registration, such as magnetic resonance imaging or computed tomography, due to various factors. These include gigapixel size of images, variations in appearance between differently stained tissues, changes in structure and morphology between non-consecutive sections, and the presence of artefacts, tears, and deformations. Currently, there is a noticeable gap in the literature regarding a review of the current approaches and their limitations, as well as the challenges and opportunities they present. We aim to provide a comprehensive understanding of the available approaches and their application for various purposes. Furthermore, we investigate current deep learning methods used for WSI registration, emphasising their diverse methodologies. We examine the available datasets and explore tools and software employed in the field. Finally, we identify open challenges and potential future trends in this area of research.
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。
