用深度学习自动测算鼻窦手术风险评分，省去人工测量
Automated Estimation of Anatomical Risk Metrics for Endoscopic Sinus Surgery Using Deep Learning
- 通过热图回归定位关键解剖点，自动估算风险分数
- 误差低至0.506mm（Keros）、4.516°（Gera）
- 适合需要高效术前评估的耳鼻喉科医生
内窥镜鼻窦手术需仔细评估颅底解剖结构以降低脑脊液漏等风险。如Keros、Gera和泰国-马来西亚-新加坡（TMS）评分等解剖风险评分提供了标准化方法，但需在冠状位CT或锥形束CT扫描上进行耗时的手动测量。本文提出一种自动化深度学习流程，通过热图回归定位关键解剖标志点，估算这些风险评分。我们对比了直接方法与专为全局到局部学习设计的策略，结果显示：对Keros评分的平均绝对误差为0.506mm，Gera评分为4.516°，TMS分类的误差分别为0.802mm和0.777mm。
原文摘要 · Abstract (English)
Endoscopic sinus surgery requires careful preoperative assessment of the skull base anatomy to minimize risks such as cerebrospinal fluid leakage. Anatomical risk scores like the Keros, Gera and Thailand-Malaysia-Singapore score offer a standardized approach but require time-consuming manual measurements on coronal CT or CBCT scans. We propose an automated deep learning pipeline that estimates these risk scores by localizing key anatomical landmarks via heatmap regression. We compare a direct approach to a specialized global-to-local learning strategy and find mean absolute errors on the relevant anatomical measurements of 0.506mm for the Keros, 4.516° for the Gera and 0.802mm / 0.777mm for the TMS classification.
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