用概率图引导双分支融合，自动评估胃内容物以降低麻醉风险。
REASON: Probability map-guided dual-branch fusion framework for gastric content assessment
- 分两阶段：先生成抑制伪影的胃部概率图，再融合两个体位图像特征
- 在自建数据集上显著优于现有最先进方法
- 适合临床麻醉前风险评估，提升自动化与准确性
从超声图像准确评估胃内容物对分层管理全身麻醉诱导时的误吸风险至关重要。传统方法依赖手动勾画胃窦并使用经验公式，效率与精度均存在明显局限。为此，提出一种新型两阶段概率图引导双分支融合框架（REASON）。第一阶段，分割模型生成抑制伪影并突出胃部解剖结构的概率图；第二阶段，双分支分类器融合右后卧位（RLD）和仰卧位（SUP）两种标准视角信息，增强特征判别能力。在自建数据集上的实验结果表明，该框架显著优于当前最先进的方法。该框架为术前误吸风险自动化评估提供了更稳健、高效且精准的解决方案，具有广阔临床应用前景。
原文摘要 · Abstract (English)
Accurate assessment of gastric content from ultrasound is critical for stratifying aspiration risk at induction of general anesthesia. However, traditional methods rely on manual tracing of gastric antra and empirical formulas, which face significant limitations in both efficiency and accuracy. To address these challenges, a novel two-stage probability map-guided dual-branch fusion framework (REASON) for gastric content assessment is proposed. In stage 1, a segmentation model generates probability maps that suppress artifacts and highlight gastric anatomy. In stage 2, a dual-branch classifier fuses information from two standard views, right lateral decubitus (RLD) and supine (SUP), to improve the discrimination of learned features. Experimental results on a self-collected dataset demonstrate that the proposed framework outperforms current state-of-the-art approaches by a significant margin. This framework shows great promise for automated preoperative aspiration risk assessment, offering a more robust, efficient, and accurate solution for clinical practice.
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