REASON：用于胃内容物评估的概率图引导双分支融合框架
REASON: Probability map-guided dual-branch fusion framework for gastric content assessment
AI总结：
针对全身麻醉诱导时超声胃内容物评估效率与准确性受限的问题，提出两阶段概率图引导双分支融合框架REASON，通过分割模型生成概率图抑制伪影并融合RLD与SUP双切面特征，在自采集数据集上显著优于现有方法，为术前误吸风险自动化评估提供鲁棒高效方案。
摘要：
从超声中准确评估胃内容物对于全身麻醉诱导时的误吸风险分层至关重要。然而，传统方法依赖胃窦的手动追踪和经验公式，在效率和准确性方面均面临显著局限。为应对这些挑战，提出了一种用于胃内容物评估的新型两阶段概率图引导双分支融合框架（REASON）。在第一阶段，分割模型生成概率图以抑制伪影并突出胃部解剖结构。在第二阶段，双分支分类器融合来自两个标准切面的信息，即右侧卧位（RLD）和仰卧位（SUP），以提升所学特征的判别能力。在自采集数据集上的实验结果表明，所提框架以显著优势优于当前最先进的方法。该框架在自动化术前误吸风险评估方面展现出巨大潜力，为临床实践提供了更鲁棒、高效且准确的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
