用关键点对齐组织切片，实现高精度虚拟染色
K-Stain: Keypoint-Driven Correspondence for H&E-to-IHC Virtual Staining
- 以关键点建立空间对应关系，提升图像对齐精度
- 在多个数据集上优于现有方法，生成图像更真实一致
- 适合病理图像分析、数字病理学研究者使用
虚拟染色可将苏木精-伊红（H&E）图像转换为免疫组化（IHC）图像，避免昂贵的化学染色过程。然而，现有方法常因组织切片错位而难以有效利用空间信息。为此，我们引入关键点作为空间对应的鲁棒指标，提升合成IHC图像的结构一致性。提出K-Stain框架，包含三个部分：(1) 分层空间关键点检测器（HSKD）用于识别染色图像中的关键点；(2) 关键点感知增强生成器（KEG），在图像生成中融合关键点信息；(3) 关键点引导判别器（KGD），增强判别器对空间细节的敏感性。该方法利用相邻切片的上下文信息，显著提升合成图像的保真度。大量实验表明，K-Stain在定量指标和视觉质量上均优于当前最优方法。
原文摘要 · Abstract (English)
Virtual staining offers a promising method for converting Hematoxylin and Eosin (H&E) images into Immunohistochemical (IHC) images, eliminating the need for costly chemical processes. However, existing methods often struggle to utilize spatial information effectively due to misalignment in tissue slices. To overcome this challenge, we leverage keypoints as robust indicators of spatial correspondence, enabling more precise alignment and integration of structural details in synthesized IHC images. We introduce K-Stain, a novel framework that employs keypoint-based spatial and semantic relationships to enhance synthesized IHC image fidelity. K-Stain comprises three main components: (1) a Hierarchical Spatial Keypoint Detector (HSKD) for identifying keypoints in stain images, (2) a Keypoint-aware Enhancement Generator (KEG) that integrates these keypoints during image generation, and (3) a Keypoint Guided Discriminator (KGD) that improves the discriminator's sensitivity to spatial details. Our approach leverages contextual information from adjacent slices, resulting in more accurate and visually consistent IHC images. Extensive experiments show that K-Stain outperforms state-of-the-art methods in quantitative metrics and visual quality.
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