SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation
This addresses the challenge of precise tool identification in medical imaging and robotic surgery, potentially enhancing surgical accuracy and patient care outcomes, though it appears incremental as it combines existing super-resolution and segmentation techniques.
The paper tackles the problem of identifying surgical instruments in low-resolution stereo endoscopic images by proposing SEGSRNet, which enhances image clarity and segmentation accuracy, outperforming current models in metrics like Dice, IoU, PSNR, and SSIM.
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 outperforms current models including Dice, IoU, PSNR, and SSIM, SEGSRNet where it produces clearer and more accurate images for stereo endoscopic surgical imaging. SEGSRNet can provide image resolution and precise segmentation which can significantly enhance surgical accuracy and patient care outcomes.