TR7Net: A Hybrid Transformer-CNN Framework for Endoscopic Image Segmentation with Validation on Spinal Surgery

In endoscopic surgery, accurate segmentation of medical images is indispensable for surgical planning, navigation, and real-time guidance. However, existing segmentation methods often fall short in capturing complex anatomical details and long-range contextual information, which are critical for precise segmentation. To address these challenges, we introduce TR7Net TransUNet-RSU7-Network , a novel hybrid deep learning framework that integrates the strengths of TransUNet and RSU7 architectures. TransUNet excels in global context modeling through its transformer encoder-decoder structure, while RSU7 is renowned for its robust feature extraction capabilities, particularly in handling intricate image features. TR7Net synergizes these two architectures to achieve superior segmentation performance. Extensive experiments on spinal endoscopic datasets demonstrate that TR7Net outperforms both TransUNet and nnUNet regarding segmentation accuracy and robustness in surgical scenarios. This work presents a significant advancement in medical image segmentation for spinal endoscopic surgery, offering a more precise and reliable solution for surgical assistance.