Detecting anatomical structures in surgical video is essential for intraoperative safety frameworks such as the Critical View of Myopectineal Orifice (CVMPO) in inguinal hernia repair. While prominent structures like the Cooper's Ligament and Triangle of Doom are reliably detected by standard methods, smaller structures such as the epigastric vessels remain challenging due to their visual ambiguity and intermittent visibility. We observe that the spatial relationship between structures is anatomically constrained, and propose a Gaussian Spatial Prior (GSP) module that encodes this relationship as a compact, parametric bias injected into the self-attention of a DAB-DETR decoder. The prior is computed offline from training annotations as a small set of frozen Gaussian parameters and recomputed at each decoder layer using the iteratively refined reference points. On a dataset of inguinal hernia repair videos with 5-fold cross-validation, GSP improves dependent class detection by $+33.5\%$ ($\text{AP}_{50}$) over DAB-DETR and $+53.9\%$ over YOLOv26, while also improving anchor detection by $+6.0\%$. These gains are statistically significant across all folds ($p=0.012$, paired $t-$test).

翻译：检测手术视频中的解剖结构对于腹股沟疝修补术中的关键安全框架（如肌耻骨孔临界视野CVMPO）至关重要。虽然库珀韧带和死亡三角等显著结构可通过标准方法可靠检测，但腹壁上血管等较小结构因其视觉模糊性和间歇性可见性仍具挑战性。我们观察到各结构间的空间关系受解剖学约束，据此提出高斯空间先验（GSP）模块，将该关系编码为紧凑参数化偏置，注入DAB-DETR解码器的自注意力机制中。该先验从训练标注离线计算为一组冻结的高斯参数，并在每个解码器层利用迭代精化参考点重新计算。在腹股沟疝修补术视频数据集上进行的5折交叉验证表明，相较DAB-DETR和YOLOv26，GSP分别使依赖类检测提升+33.5%（AP50）和+53.9%，同时锚点检测提升+6.0%。所有折的改善均具有统计学显著性（p=0.012，配对t检验）。