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.
Abstract (translated)
SEGSRNet 解决了在低分辨率立体视频内镜图像中精确识别手术器械的挑战，这是医疗影像和机器人手术中常见的问题。我们创新的方法通过在分割之前应用最先进的超分辨率技术来提高图像清晰度和分割准确性，从而确保为更精确的分割提供更高质量的输入。SEGSRNet 结合先进的特征提取和关注机制与空间处理来增强图像细节，这对准确工具识别在医学图像中非常重要。与当前模型包括 Dice、IoU、PSNR 和 SSIM 相比，我们的提出的模型在立体内镜手术视频中的图像清晰度和准确性方面表现出色。SEGSRNet 可以在图像分辨率和解剖细节上提供更高的准确性和更高质量的分割，从而显著提高手术准确性和患者护理结果。
URL
https://arxiv.org/abs/2404.13330
