arXiv 2506.231842025-06-29eess.IVcs.AIcs.CV

用于非配对虚拟组织学染色的基于分数的扩散模型

Score-based Diffusion Model for Unpaired Virtual Histology Staining

Department of Health Technology and Informatics, Hong Kong Polytechnic University, China（香港理工大学健康科技与信息学系）；Department of Clinical Neurosciences, University of Cambridge, UK（剑桥大学临床神经科学系）；School of Science and Engineering, University of Dundee, UK（邓迪大学科学与工程学院）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK（剑桥大学应用数学与理论物理系）

Anran Liu, Xiaofei Wang, Jing Cai, Chao Li

备注：: 11 pages, 3 figures

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对非配对虚拟染色中结构与风格解耦及非对齐一致性难题，提出互信息引导的基于分数的扩散模型，通过全局能量函数、定制逆向扩散和局部对比学习实现高保真、可控的H&E到IHC转换。

摘要：

H&E染色能够可视化组织学，但缺乏对诊断标志物的特异性。IHC染色提供靶向蛋白质的染色，但受限于组织可用性和抗体特异性。虚拟染色，即在保留组织结构的同时通过计算将H&E图像转换为其IHC对应图像，有望实现高效的IHC生成。现有虚拟染色方法仍面临关键挑战：1）染色风格与组织结构的有效分解；2）适应多样组织与蛋白质的可控染色过程；3）严格的建模以处理非像素对齐的H&E与IHC图像的结构一致性。本研究提出一种用于非配对虚拟染色的互信息（MI）引导的基于分数的扩散模型。具体而言，我们设计了：1）一个全局MI引导的能量函数，用于解耦跨模态的组织结构与染色特征；2）一种新颖的时间步定制的逆向扩散过程，用于精确控制染色强度与结构重建；3）一种局部MI驱动的对比学习策略，以确保H&E-IHC图像在细胞层面的结构一致性。大量实验证明了该方法优于现有最先进方法，凸显了其生物医学潜力。代码将在论文被接收后开源。

英文摘要：

Hematoxylin and eosin (H&E) staining visualizes histology but lacks specificity for diagnostic markers. Immunohistochemistry (IHC) staining provides protein-targeted staining but is restricted by tissue availability and antibody specificity. Virtual staining, i.e., computationally translating the H&E image to its IHC counterpart while preserving the tissue structure, is promising for efficient IHC generation. Existing virtual staining methods still face key challenges: 1) effective decomposition of staining style and tissue structure, 2) controllable staining process adaptable to diverse tissue and proteins, and 3) rigorous structural consistency modelling to handle the non-pixel-aligned nature of paired H&E and IHC images. This study proposes a mutual-information (MI)-guided score-based diffusion model for unpaired virtual staining. Specifically, we design 1) a global MI-guided energy function that disentangles the tissue structure and staining characteristics across modalities, 2) a novel timestep-customized reverse diffusion process for precise control of the staining intensity and structural reconstruction, and 3) a local MI-driven contrastive learning strategy to ensure the cellular level structural consistency between H&E-IHC images. Extensive experiments demonstrate the our superiority over state-of-the-art approaches, highlighting its biomedical potential. Codes will be open-sourced upon acceptance.

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。