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

arXiv:2404.13330v1 Announce Type: cross
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 …

abstract accuracy art arxiv challenge cs.cv eess.iv framework image images imaging inputs issue low medical medical imaging quality resolution robotic robotic surgery segmentation state surgery type