Double‐Layer Graph Attention Networks for Parathyroid Detection

ABSTRACT Due to the importance of the parathyroid glands (PG) for health, detecting and preserving them during endoscopic thyroid surgery is vital. However, existing parathyroid detection methods face issues from colour variations, target deformation, blurriness, and lighting effects in complex surgical environments. Therefore, they fail to extract high‐quality features and perform poorly in our detection tasks. The essential reasons for these issues are two‐fold: (A) ignoring the spatial relation among targets and (B) failing to identify the shapes, colours, and positions of targets, mainly due to insufficient utilisation of depth information , especially under lighting variations and occlusions, which are unavoidably prone to false or missed detection. To better discover and exploit the inter‐target spatial relation (solving A), inspired by the power of graph neural networks on dependency modelling, we explore an effective graph‐based framework specifically designed for PG detection. Specifically, we propose a novel double‐layer graph attention network (i.e., DL‐GAT for short), which explicitly facilitates local augmentation through the identification of key visual features (e.g., texture and shape) and global interactions. Besides, it also has merits in robustly combating image blur and better differentiating PG targets and background parts, thus improving detection precision. On the other hand, to solve B and better incorporate depth knowledge , we further propose a depth augmentation component, which can adaptively capture the intrinsic geometrical features of targets based on depth information and thus significantly improve light intensity robustness and naturally enhance generalisability. Moreover, because we lack a thyroid endoscopy surgery benchmark to evaluate and compare the performance of models for this task, we meticulously established a novel data set from 838 real surgeries performed (via the fully laparoscopic thoracic‐breast approach) at the Fujian Medical University Union Hospital. Extensive experiments show that our framework achieves superior PG detection accuracy compared to its current state‐of‐the‐art counterparts while maintaining real‐time efficiency.