Ceachi Bogdan, Muresan Filip, Trascau Mihai, Florea Adina Magda
Faculty of Automatic Control and Computers, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.
Victor Babeş National Institute of Research and Development in Pathology & Biomedical Sciences, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania.
Cancers (Basel). 2025 Sep 5;17(17):2918. doi: 10.3390/cancers17172918.
: 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. 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. 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.
全切片图像（WSIs）在病理学中对于组织切片数字化至关重要，使病理学家和人工智能模型能够在千兆像素尺度上分析癌症模式。然而，它们的大尺寸包含了伪像和非组织区域，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工作流程中的关键第一步，以聚焦于相关区域，但深度学习检测方法需要大量的人工标注。方法：本研究在来自九个癌症队列的3322张TCGA WSI上对四种缩略图级组织检测方法进行了基准测试，这四种方法分别是大津阈值法、K均值聚类、我们新颖的无标注双通混合法以及GrandQC的UNet++，评估了准确性、速度和效率。双通法实现了0.826的平均交并比，非常接近深度学习GrandQC模型的0.871，同时在CPU上处理每张幻灯片仅需0.203秒，明显快于GrandQC在相同硬件上每张幻灯片2.431秒的处理速度。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、可扩展的缩略图级组织检测。可扩展的、无标注的双通工作流程减少了计算瓶颈，促进了高通量WSI预处理，能够更快且更具成本效益地将人工智能集成到临床病理学和研究工作流程中。将双通法与既定方法进行比较，该基准测试证明了其作为一种快速、稳健且无标注的监督方法替代方案的新颖性。
