CountPath：实现数字病理学中组织碎片计数的自动化
CountPath: Automating Fragment Counting in Digital Pathology
AI总结：
本文提出 CountPath，利用 YOLOv9 与 Vision Transformer 自动核对病理载玻片上的标本碎片数量，其 86% 的准确率处于专家观察者差异范围内。
摘要：
医学图像质量控制是数字病理学的关键组成部分，可确保诊断图像达到所需标准。该流程中的一项分析前任务是核对标本碎片数量，即确认载玻片上的碎片数量与大体报告中记录的数量一致。这一步骤非常重要，可确保载玻片包含来自取材过程的适当诊断材料，从而保证后续显微镜检查和诊断的准确性。传统上，该评估由人工完成，需要大量时间和精力，并且由于其主观性而存在显著差异。为应对这些挑战，本研究探索了一种使用 YOLOv9 和 Vision Transformer 模型进行碎片计数的自动化方法。结果表明，该自动化系统达到了与专家评估相当的性能水平，为人工计数提供了可靠且高效的替代方案。此外，我们还给出了关于观察者间差异的发现，表明自动化方法达到了 86% 的准确率，处于专家之间观察到的差异范围（82%–88%）之内，进一步支持了其整合进常规病理学工作流程的潜力。
英文摘要：
Quality control of medical images is a critical component of digital pathology, ensuring that diagnostic images meet required standards. A pre-analytical task within this process is the verification of the number of specimen fragments, a process that ensures that the number of fragments on a slide matches the number documented in the macroscopic report. This step is important to ensure that the slides contain the appropriate diagnostic material from the grossing process, thereby guaranteeing the accuracy of subsequent microscopic examination and diagnosis. Traditionally, this assessment is performed manually, requiring significant time and effort while being subject to significant variability due to its subjective nature. To address these challenges, this study explores an automated approach to fragment counting using the YOLOv9 and Vision Transformer models. Our results demonstrate that the automated system achieves a level of performance comparable to expert assessments, offering a reliable and efficient alternative to manual counting. Additionally, we present findings on interobserver variability, showing that the automated approach achieves an accuracy of 86%, which falls within the range of variation observed among experts (82-88%), further supporting its potential for integration into routine pathology workflows.
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