使用视觉基础模型对组织病理学图像碎片进行语义拼接
Semantic Mosaicing of Histo-Pathology Image Fragments using Visual Foundation Models
AI总结：
本文提出SemanticStitcher，利用视觉基础模型的特征表示进行组织病理学碎片拼接，通过语义匹配和姿态估计生成全切片，在三个数据集上优于现有方法。
摘要：
在组织病理学中，组织样本通常大于标准显微镜载玻片，因此需要拼接多个碎片以处理整个结构（如肿瘤）。自动化拼接是扩展分析规模的前提，但由于制备过程中可能出现的组织损失、不均匀的形态畸变、染色不一致、载玻片上错位导致的缺失区域或组织边缘磨损，这一任务具有挑战性。这些因素限制了使用边界形状匹配算法重建人工全切片（WMS）的最先进拼接方法。在此，我们引入SemanticStitcher，利用从视觉组织病理学基础模型中提取的潜在特征表示来识别不同碎片中的相邻区域。基于大量语义匹配候选的稳健姿态估计，将多个碎片拼接成马赛克以形成WMS。在三个不同的组织病理学数据集上的实验表明，SemanticStitcher能够实现稳健的WMS拼接，并在正确边界匹配方面持续优于现有最先进方法。
英文摘要：
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.
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。
