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

Mansoor Hayat, Supavadee Aramvith, Titipat Achakulvisut

TL;DR

SEGSRNet tackles the challenge of instrument identification in low-resolution stereo endoscopic images by first applying a specialized super-resolution stage with cross-view attention and multi-scale refinement, followed by a segmentation module based on SP P-LinkNet-34. It integrates biPAM-inspired cross-view interaction, occlusion handling, and Residual Dense Blocks to produce high-quality SR images and accurate instrument segmentation. On MICCAI 2018 and EndoVis 2017 datasets, SEGSRNet yields superior PSNR/SSIM and IoU/Dice metrics compared with baselines, demonstrating improved stereo consistency and multi-class segmentation. The approach offers practical potential to enhance surgical accuracy and patient outcomes by providing clearer imagery and reliable instrument delineation in robotic surgery.

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.

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

TL;DR

SEGSRNet tackles the challenge of instrument identification in low-resolution stereo endoscopic images by first applying a specialized super-resolution stage with cross-view attention and multi-scale refinement, followed by a segmentation module based on SP P-LinkNet-34. It integrates biPAM-inspired cross-view interaction, occlusion handling, and Residual Dense Blocks to produce high-quality SR images and accurate instrument segmentation. On MICCAI 2018 and EndoVis 2017 datasets, SEGSRNet yields superior PSNR/SSIM and IoU/Dice metrics compared with baselines, demonstrating improved stereo consistency and multi-class segmentation. The approach offers practical potential to enhance surgical accuracy and patient outcomes by providing clearer imagery and reliable instrument delineation in robotic surgery.

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.
Paper Structure (12 sections, 3 equations, 4 figures, 2 tables)

This paper contains 12 sections, 3 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Research Methodology
  3. Super-Resolution Part
  4. Feature Extraction and Refinement Blocks
  5. Cross-View Feature Interaction Module
  6. Reconstruction Block
  7. Segmentation Part
  8. Datasets
  9. Training Settings
  10. Experimental Results
  11. Quantitative and Qualitative Results
  12. Conclusion

Figures (4)

  • Figure 1: Proposed SEGSRNet Architecture. A. SEGSRNet architecture consists of super-resolution and segmentation modules. B. Proposed cross-view attention module and residual dense block in super-resolution framework.
  • Figure 2: SPP-LinkNet34 structure highlighting the encoder-decoder network with spatial pyramid pooling for enhanced multi-scale feature extraction in image segmentation tasks.
  • Figure 3: Assessment of the Visual Quality of High-Resolution Images Created Through Image Super-Resolution Techniques at a $\times4$ Scale Factor.
  • Figure 4: Comparative Evaluation of Segmentation Performance: Our Model versus Current State-of-the-Art Models.