用视觉基础模型实现病理切片碎片的语义拼接，提升整体图像重建精度。
Semantic Mosaicing of Histo-Pathology Image Fragments using Visual Foundation Models
- 基于视觉基础模型提取语义特征，识别不同切片间的相邻区域。
- 通过大量语义匹配候选点实现鲁棒姿态估计，拼接出完整组织图像。
- 在3个数据集上优于现有方法，尤其在边界匹配准确率上显著提升。
在病理学中，组织样本常大于标准显微镜载玻片，需拼接多个片段以分析完整结构（如肿瘤）。自动拼接是规模化分析的前提，但受制于制备过程中的组织丢失、形态扭曲不均、染色差异、滑动错位导致的缺失区域或组织边缘破损等问题，现有基于边界形状匹配的拼接方法难以重建真实全幅切片（WMS）。本文提出SemanticStitcher，利用视觉病理基础模型生成的潜在特征表示，识别不同片段间的邻近区域。基于大量语义匹配候选点进行鲁棒姿态估计，实现多片段拼接形成全幅切片。在三个不同病理数据集上的实验表明，SemanticStitcher能稳定生成高质量全幅拼接图像，在正确边界匹配率上持续优于当前最优方法。
原文摘要 · Abstract (English)
In histopathology, tissue samples are often larger than a standard microscope slide, making stitching of multiple fragments necessary to process entire structures such as tumors. Automated stitching is a prerequisite for scaling analysis, but is challenging due to possible tissue loss during preparation, inhomogeneous morphological distortion, staining inconsistencies, missing regions due to misalignment on the slide, or frayed tissue edges. This limits state-of-the-art stitching methods using boundary shape matching algorithms to reconstruct artificial whole mount slides (WMS). Here, we introduce SemanticStitcher using latent feature representations derived from a visual histopathology foundation model to identify neighboring areas in different fragments. Robust pose estimation based on a large number of semantic matching candidates derives a mosaic of multiple fragments to form the WMS. Experiments on three different histopathology datasets demonstrate that SemanticStitcher yields robust WMS mosaicing and consistently outperforms the state of the art in correct boundary matches.
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