用视觉Transformer分三步自动识别肠道神经节细胞，辅助诊断先天性巨结肠。
Automated Histopathologic Assessment of Hirschsprung Disease Using a Multi-Stage Vision Transformer Framework
- 分阶段处理：先定位肌层，再分割肌间神经丛，最后检测神经节细胞。
- 神经节细胞检测召回率达89.1%，在高置信标注下精度达62.1%。
- 方法模拟病理医生思路，适合数字病理辅助诊断场景。
先天性巨结肠症表现为肌间神经丛缺乏神经节细胞，因此准确识别神经节细胞对诊断至关重要。本文提出一种三阶段分析框架，模拟病理医生的诊断流程。该框架基于Vision Transformer模型（ViT-B/16），依次完成肌层分割、肌间神经丛分割及解剖学有效区域内神经节细胞的检测。研究使用30张结肠组织全切片图像，每张图像均包含肌层、神经丛和神经节细胞的人工标注。各阶段采用5折交叉验证，结合分辨率特定的切块策略与定制后处理，确保解剖一致性。结果表明，肌层分割的Dice系数达89.9%，神经丛包含率100%；神经丛分割的召回率为94.8%，精确率为84.2%，神经节包含率99.7%。对于高置信度标注的神经节细胞，模型精度为62.1%，召回率为89.1%；考虑所有标注神经节细胞时，总体精度为67.0%。结果表明，基于ViT的模型能有效利用全局组织上下文并捕捉微小尺度的细胞形态特征，即使在复杂的组织结构中亦表现良好。该多阶段方法具有显著潜力，可降低观察者间差异，支持数字病理工作流。临床影响将在未来更大规模多中心数据集与更多专家标注中进一步评估。
原文摘要 · Abstract (English)
Hirschsprung Disease is characterized by the absence of ganglion cells in the myenteric plexus. Therefore, the correct identification of ganglion cells is crucial for diagnosing Hirschsprung disease. We introduce a three-stage analysis framework that mimics the pathologist's diagnostic approach. The framework, based on a Vision Transformer model (ViT-B/16), sequentially segments the muscularis propria, segments the myenteric plexus, and detects ganglion cells within anatomically valid regions. 30 whole-slide images of colon tissue were used, each containing manual annotations of muscularis, plexus, and ganglion cells. A 5-fold cross-validation scheme was applied to each stage, along with resolution-specific tiling strategies and tailored postprocessing to ensure anatomical consistency. The proposed method achieved a Dice coefficient of 89.9% and a Plexus Inclusion Rate of 100% for muscularis segmentation. Plexus segmentation reached a recall of 94.8%, a precision of 84.2% and a Ganglia Inclusion Rate of 99.7%. For ganglion cells annotated with high certainty, the model achieved 62.1\% precision and 89.1% recall. When considering all annotated ganglion cells, regardless of certainty level, the overall precision was 67.0%. These results indicate that ViT-based models are effective at leveraging global tissue context and capturing cellular morphology at small scales, even within complex histological tissue structures. This multi-stage methodology has great potential to support digital pathology workflows by reducing inter-observer variability and assisting in the evaluation of Hirschsprung disease. The clinical impact will be evaluated in future work with larger multi-center datasets and additional expert annotations.
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