AtlasPatch: 高效组织检测与高通量切片提取用于大规模计算病理学
AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology
AI总结：
AtlasPatch通过高效组织检测和高通量切片提取，显著提升大规模计算病理学的处理效率与性能。
摘要：
全切片图像（WSI）预处理，包括组织检测后接续切片提取，是人工智能驱动的计算病理学的基础，但仍然是扩展到大规模和异质队列的主要瓶颈。我们提出了AtlasPatch，一个可扩展的框架，将基础模型的组织检测与高通量切片提取相结合，在最小的计算开销下实现。我们的组织检测器在精度（0.986）上表现优异，并在变化的组织条件（例如亮度、碎片化、边界定义、组织异质性）和常见伪影（例如笔/墨迹标记、扫描器条纹）中保持稳健。这种稳健性得益于我们注释的、异质的多队列训练集（约30,000张WSI缩略图）结合了高效的Segment-Anything（SAM）模型适应。AtlasPatch还通过广泛使用的深度学习管道将端到端的WSI预处理时间减少了高达16倍，而不影响下游任务性能。AtlasPatch工具是开源的，能够高效并行化以实际部署，并支持保存提取的切片或将其流式传输到常见的特征提取模型中进行实时嵌入，使其适用于病理部门（组织检测和质量控制）和AI研究人员（数据集创建和模型训练）。AtlasPatch软件包可在https://github.com/AtlasAnalyticsLab/AtlasPatch上获得。
英文摘要：
Whole-slide image (WSI) preprocessing, including tissue detection and patch extraction, is critical computational pathology, yet remains a major bottleneck for large-scale workflows. Existing methods often rely either on threshold-based heuristics that are sensitive to staining variations, tissue fragmentation, and artifacts, or on patch-wise deep learning pipelines with substantially higher computational cost. We present AtlasPatch, a scalable high-throughput WSI preprocessing method built around a foundation-model-based tissue detector that operates at thumbnail resolution: a single thumbnail-level forward pass yields a tissue mask that directly guides patch coordinate generation at the target desired magnification, avoiding repeated patch-level inference. The proposed detector's robustness and efficiency is driven by two coupled contributions: (i) a parameter-efficient adaptation of the SAM2 foundation model that updates only its layer-normalization parameters (0.076% of model weights), and (ii) a curated and semi-manually annotated multi-cohort dataset of 30,000 WSI thumbnail-mask pairs deliberately spanning multiple organs, scanners, tissue appearances, and artifacts. The detector is coupled with pyramid-aware contour mapping from thumbnail to full-resolution slide coordinates, enabling direct patch coordinate generation at the target magnification and parallelized high-throughput patch extraction. AtlasPatch's tissue detection achieves a precision of 0.986 and remains robust across slide variations. Compared with widely used deep-learning preprocessing methods, AtlasPatch is up to 16x faster while preserving downstream multiple-instance learning performance across six slide-level classification tasks. These results position AtlasPatch as a frontier of efficient preprocessing in large-scale computational pathology and pathology foundation models.
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