Improving Surgical Tool Segmentation under Bleeding Corruption via Specialized Augmentation Strategy

Abstract

Artificial intelligence (AI) shows great potential for improving surgical efficiency, precision, and autonomy in surgical robotic systems. However, the robustness of deep learning-based algorithms remains a critical challenge as the surgical environments shows much variance in real application. Most deep learning-based segmentation models, though highly effective on benchmarking datasets, often fail during unforeseen nonadversarial corruptions such as occlusions, bleeding, or low brightness. In this study, we introduce a domain-specific augmentation strategy to enhance model robustness against possible surgical corruptions that is not seen in the training data. Our method simulates key corruptions, including blood simulation, brightness adjustment, and contrast adjustment. Based on the SegSTRONG-C benchmark, we evaluate a baseline U-Net model on a binary surgical tool segmentation task. While the baseline shows strong performance on clean images, its accuracy drops substantially on the corrupted test data. Incorporating our proposed augmentations significantly improves performance on corrupted inputs while preserving accuracy on the clean domain. These findings underscore the importance of specific augmentation for models robustness and demonstrate a practical pathway toward more reliable and generalizable segmentation models for real-world surgical robotics applications

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