Gaussian Spatial Priors for Anatomy-Aware Object Detection in Surgical Videos

2026-06-12

AI Summary

This study addresses the challenge of accurately detecting small-scale anatomical structures—such as the inferior epigastric vessels—in laparoscopic inguinal hernia repair videos, where visual blur and intermittent visibility hinder reliable identification. To tackle this, the authors propose a Gaussian Spatial Prior (GSP) module that, for the first time, explicitly models spatial constraints among anatomical structures as learnable, compact Gaussian parameters. These priors are integrated into the self-attention mechanism of the DAB-DETR decoder and dynamically updated through iteratively refined reference points. Evaluated on a surgical video dataset of inguinal hernia repairs, the method significantly improves detection performance: compared to DAB-DETR and YOLOv26, it achieves relative gains of 33.5% and 53.9% in class-specific AP50, respectively, and enhances landmark detection accuracy by 6.0% (p=0.012), demonstrating the efficacy of anatomy-aware spatial priors for fine-grained object detection in surgical scenes.

Abstract

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).

Problem

• anatomical object detection
• surgical video analysis
• visual ambiguity
• intermittent visibility
• small anatomical structures

Innovation

• Gaussian Spatial Prior
• anatomy-aware detection
• surgical video analysis
• DAB-DETR
• spatial constraints