SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation

Abstract

SEGSRNet addresses the challenge of precisely identifying surgical instruments in low-resolution stereo endoscopic images, a common issue in medical imaging and robotic surgery. Our innovative framework enhances image clarity and segmentation accuracy by applying state-of-the-art super-resolution techniques before segmentation. This ensures higher-quality inputs for more precise segmentation. SEGSRNet combines advanced feature extraction and attention mechanisms with spatial processing to sharpen image details, which is significant for accurate tool identification in medical images. Our proposed model, SEGSRNet, surpasses existing models in evaluation metrics including PSNR and SSIM for super-resolution tasks, as well as IoU and Dice Score for segmentation. SEGSRNet can provide image resolution and precise segmentation which can significantly enhance surgical accuracy and patient care outcomes.