{"ID":2839116,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16430","arxiv_id":"2511.16430","title":"Graph Neural Networks for Surgical Scene Segmentation","abstract":"Purpose: Accurate identification of hepatocystic anatomy is critical to preventing surgical complications during laparoscopic cholecystectomy. Deep learning models often struggle with occlusions, long-range dependencies, and capturing the fine-scale geometry of rare structures. This work addresses these challenges by introducing graph-based segmentation approaches that enhance spatial and semantic understanding in surgical scene analyses. Methods: We propose two segmentation models integrating Vision Transformer (ViT) feature encoders with Graph Neural Networks (GNNs) to explicitly model spatial relationships between anatomical regions. (1) A static k Nearest Neighbours (k-NN) graph with a Graph Convolutional Network with Initial Residual and Identity Mapping (GCNII) enables stable long-range information propagation. (2) A dynamic Differentiable Graph Generator (DGG) with a Graph Attention Network (GAT) supports adaptive topology learning. Both models are evaluated on the Endoscapes-Seg50 and CholecSeg8k benchmarks. Results: The proposed approaches achieve up to 7-8% improvement in Mean Intersection over Union (mIoU) and 6% improvement in Mean Dice (mDice) scores over state-of-the-art baselines. It produces anatomically coherent predictions, particularly on thin, rare and safety-critical structures. Conclusion: The proposed graph-based segmentation methods enhance both performance and anatomical consistency in surgical scene segmentation. By combining ViT-based global context with graph-based relational reasoning, the models improve interpretability and reliability, paving the way for safer laparoscopic and robot-assisted surgery through a precise identification of critical anatomical features.","short_abstract":"Purpose: Accurate identification of hepatocystic anatomy is critical to preventing surgical complications during laparoscopic cholecystectomy. Deep learning models often struggle with occlusions, long-range dependencies, and capturing the fine-scale geometry of rare structures. This work addresses these challenges by i...","url_abs":"https://arxiv.org/abs/2511.16430","url_pdf":"https://arxiv.org/pdf/2511.16430v1","authors":"[\"Yihan Li\",\"Nikhil Churamani\",\"Maria Robu\",\"Imanol Luengo\",\"Danail Stoyanov\"]","published":"2025-11-20T14:58:29Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[\"Vision Transformer\",\"Graph Neural Network\",\"Transformer\"]","has_code":false}