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

TL;DR

SEGSRNet is a novel framework that improves stereo endoscopic image resolution and segmentation accuracy, enhancing surgical precision by integrating super-resolution techniques with advanced feature extraction and attention mechanisms.

Contribution

It introduces a new model combining super-resolution and segmentation for stereo endoscopic images, outperforming existing methods in clarity and accuracy.

Findings

Outperforms current models in PSNR and SSIM metrics.

Produces clearer images for surgical instrument segmentation.

Enhances surgical accuracy and patient outcomes.

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 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.