文章：

利用经典与混合方法在TCGA癌症队列中实现全切片图像高效组织检测的基准研究

Efficient Tissue Detection in Whole-Slide Images Using Classical and Hybrid Methods: Benchmark on TCGA Cancer Cohorts

原文发布日期：5 September 2025

DOI: 10.3390/cancers17172918

类型: Article

开放获取: 是

英文摘要：

Background: Whole-slide images (WSIs) are crucial in pathology for digitizing tissue slides, enabling pathologists and AI models to analyze cancer patterns at gigapixel scale. However, their large size incorporates artifacts and non-tissue regions that slow AI processing, consume resources, and introduce errors like false positives. Tissue detection serves as the essential first step in WSI pipelines to focus on relevant areas, but deep learning detection methods require extensive manual annotations. Methods: This study benchmarks four thumbnail-level tissue detection methods—Otsu’s thresholding, K-Means clustering, our novel annotation-free Double-Pass hybrid, and GrandQC’s UNet++ on 3322 TCGA WSIs from nine cancer cohorts, evaluating accuracy, speed, and efficiency.Results:Double-Pass achieved an mIoU of 0.826—very close to the deep learning GrandQC model’s 0.871—while processing slides on a CPU in just 0.203 s per slide, markedly faster than GrandQC’s 2.431 s per slide on the same hardware. As an annotation-free, CPU-optimized method, it therefore enables efficient, scalable thumbnail-level tissue detection on standard workstations.Conclusions:The scalable, annotation-free Double-Pass pipeline reduces computational bottlenecks and facilitates high-throughput WSI preprocessing, enabling faster and more cost-effective integration of AI into clinical pathology and research workflows. Comparing Double-Pass against established methods, this benchmark demonstrates its novelty as a fast, robust and annotation-free alternative to supervised methods.

摘要翻译： 

背景：全切片图像（WSI）在病理学中对于组织切片的数字化至关重要，使病理学家和人工智能模型能够在千兆像素级别分析癌症模式。然而，其庞大的尺寸包含伪影和非组织区域，这会减慢人工智能处理速度、消耗资源并引入假阳性等错误。组织检测是WSI处理流程中必不可少的第一步，以聚焦相关区域，但深度学习检测方法需要大量手动标注。方法：本研究在来自九个癌症队列的3322张TCGA WSI上，对四种缩略图级别的组织检测方法——大津阈值法、K均值聚类、我们新颖的无标注双通道混合方法以及GrandQC的UNet++模型——进行了基准测试，评估了其准确性、速度和效率。结果：双通道方法实现了0.826的平均交并比（mIoU），与深度学习模型GrandQC的0.871非常接近，同时在CPU上处理每张切片仅需0.203秒，明显快于GrandQC在同一硬件上每张切片2.431秒的速度。作为一种无标注、CPU优化的方法，它因此能够在标准工作站上实现高效、可扩展的缩略图级别组织检测。结论：可扩展、无标注的双通道流程减少了计算瓶颈，促进了高通量WSI预处理，使人工智能能够更快、更具成本效益地整合到临床病理学和研究工作流程中。通过将双通道方法与现有方法进行比较，本基准测试证明了其作为一种快速、稳健且无标注的替代方案，相对于监督方法具有新颖性。

原文链接：

Efficient Tissue Detection in Whole-Slide Images Using Classical and Hybrid Methods: Benchmark on TCGA Cancer Cohorts