用细胞点集匹配实现多模态病理图像精准对齐
Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis
- 将细胞视为点集，结合CPD与图匹配实现跨模态对齐
- 在卵巢癌组织微阵列上实现高精度细胞级对齐
- 可从MxIF生成虚拟H&E图像，助力临床分析
病理图像对癌症研究和临床实践至关重要，多重免疫荧光（MxIF）和苏木精-伊红（H&E）染色提供互补信息。然而，由于模态差异，细胞层面的图像对齐仍具挑战。本文提出一种基于细胞分割结果的多模态图像对齐新框架。将细胞视为点集，先使用相干点漂移（CPD）进行初始对齐，再通过图匹配（GM）优化。在卵巢癌组织微阵列（TMAs）上评估，该方法实现高对齐精度，支持跨模态细胞级特征整合，并能从MxIF数据生成虚拟H&E图像，提升临床解读能力。
原文摘要 · Abstract (English)
Histopathological imaging is vital for cancer research and clinical practice, with multiplexed Immunofluorescence (MxIF) and Hematoxylin and Eosin (H&E) providing complementary insights. However, aligning different stains at the cell level remains a challenge due to modality differences. In this paper, we present a novel framework for multimodal image alignment using cell segmentation outcomes. By treating cells as point sets, we apply Coherent Point Drift (CPD) for initial alignment and refine it with Graph Matching (GM). Evaluated on ovarian cancer tissue microarrays (TMAs), our method achieves high alignment accuracy, enabling integration of cell-level features across modalities and generating virtual H&E images from MxIF data for enhanced clinical interpretation.
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