Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.01302 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908624938336256 |
|---|---|
| author | Xiao, Nu-Fnag Huang, De-Xing Wang, Le-Tian Gui, Mei-Jiang Fu, Qi Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuangyi Hou, Zeng-Guang Wang, Ying-Wei Zhou, Xiao-Hu |
| author_facet | Xiao, Nu-Fnag Huang, De-Xing Wang, Le-Tian Gui, Mei-Jiang Fu, Qi Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuangyi Hou, Zeng-Guang Wang, Ying-Wei Zhou, Xiao-Hu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01302 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | REASON: Probability map-guided dual-branch fusion framework for gastric content assessment Xiao, Nu-Fnag Huang, De-Xing Wang, Le-Tian Gui, Mei-Jiang Fu, Qi Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuangyi Hou, Zeng-Guang Wang, Ying-Wei Zhou, Xiao-Hu Computer Vision and Pattern Recognition 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. |
| title | REASON: Probability map-guided dual-branch fusion framework for gastric content assessment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.01302 |