mViSE让非编程用户也能快速检索脑组织多路免疫荧光图像中的细胞群。
mViSE: A Visual Search Engine for Analyzing Multiplex IHC Brain Tissue Images
- 分面板训练自监督编码器，通过视觉验证学习组织结构特征。
- 支持查询单个细胞、细胞对或组织区域，准确识别皮层层次和脑区。
- 开源插件，无需编程即可探索脑组织，适合生物医学研究者使用。
全切片多路脑组织成像产生海量信息密集图像，分析困难且需定制软件。我们提出一种无需编程的查询驱动策略——多路视觉搜索引擎（mViSE），学习脑组织的化学架构、细胞结构和髓鞘结构。采用分治策略将数据按相关分子标志物分组，利用自监督学习为每组训练多路编码器，并通过显式视觉确认学习效果。多个编码器可组合使用，借助信息论方法检索相似的单细胞或细胞群落。该方法可用于组织探索、划分皮层层次与脑区、无编程对比不同脑区。我们验证了mViSE在检索单细胞、邻近细胞对、组织片段，以及划分皮层层、脑区与亚区方面的有效性。mViSE作为开源QuPath插件提供。
原文摘要 · Abstract (English)
Whole-slide multiplex imaging of brain tissue generates massive information-dense images that are challenging to analyze and require custom software. We present an alternative query-driven programming-free strategy using a multiplex visual search engine (mViSE) that learns the multifaceted brain tissue chemoarchitecture, cytoarchitecture, and myeloarchitecture. Our divide-and-conquer strategy organizes the data into panels of related molecular markers and uses self-supervised learning to train a multiplex encoder for each panel with explicit visual confirmation of successful learning. Multiple panels can be combined to process visual queries for retrieving similar communities of individual cells or multicellular niches using information-theoretic methods. The retrievals can be used for diverse purposes including tissue exploration, delineating brain regions and cortical cell layers, profiling and comparing brain regions without computer programming. We validated mViSE's ability to retrieve single cells, proximal cell pairs, tissue patches, delineate cortical layers, brain regions and sub-regions. mViSE is provided as an open-source QuPath plug-in.
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