用AI自动标注手术切缘，省去传统染色，提升病理分析一致性。
Autonomous labeling of surgical resection margins using a foundation model
- 基于冻结的预训练模型+轻量分类器，自动识别组织切面特征。
- 盲测准确率达73.3%，结果与专家标注高度一致且边缘连续。
- 适合数字病理流程集成，尤其适用于需精确切缘测量的临床场景。
评估手术切缘是病理标本分析的核心环节，直接影响患者预后。当前依赖人工染色，存在涂抹不均问题，电灼伤痕常掩盖真实切缘。本文提出虚拟染色网络（VIN），可自主定位全幻灯片中的手术切面，减少对染料依赖并实现标准化切缘分析。VIN采用冻结的基础模型提取特征，结合一个小型两层多层感知机，对组织块切面区域进行像素级分类。数据集包含12个人体扁桃体组织块的120张H&E染色幻灯片，原始图像数据量约2TB，由认证病理科医师标注边界。在20张未见过组织块的盲测中，VIN生成的切缘图与专家标注在序列切片上定性吻合。定量结果显示，区域级准确率为73.3%，错误集中于局部区域，未破坏整体切缘图的连贯性。结果表明，VIN能有效捕捉电灼相关组织形态特征，提供可重复、无需染色的切缘划分方案，适用于常规数字病理流程，并支持后续切缘距离的精准测量。
原文摘要 · Abstract (English)
Assessing resection margins is central to pathological specimen evaluation and has profound implications for patient outcomes. Current practice employs physical inking, which is applied variably, and cautery artifacts can obscure the true margin on histological sections. We present a virtual inking network (VIN) that autonomously localizes the surgical cut surface on whole-slide images, reducing reliance on inks and standardizing margin-focused review. VIN uses a frozen foundation model as the feature extractor and a compact two-layer multilayer perceptron trained for patch-level classification of cautery-consistent features. The dataset comprised 120 hematoxylin and eosin (H&E) stained slides from 12 human tonsil tissue blocks, resulting in ~2 TB of uncompressed raw image data, where a board-certified pathologist provided boundary annotations. In blind testing with 20 slides from previously unseen blocks, VIN produced coherent margin overlays that qualitatively aligned with expert annotations across serial sections. Quantitatively, region-level accuracy was ~73.3% across the test set, with errors largely confined to limited areas that did not disrupt continuity of the whole-slide margin map. These results indicate that VIN captures cautery-related histomorphology and can provide a reproducible, ink-free margin delineation suitable for integration into routine digital pathology workflows and for downstream measurement of margin distances.
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