Abstract
This study proposed enhanced U-Net with GridMask (EUGNet) image augmentation techniques focused on pixel manipulation, emphasizing GridMask augmentation. This study introduces EUGNet, which incorporates GridMask augmentation to address U-Net's limitations. EUGNet features a deep contextual encoder, residual connections, class-balancing loss, adaptive feature fusion, GridMask augmentation module, efficient implementation, and multi-modal fusion. These innovations enhance segmentation accuracy and robustness, making it well-suited for medical image analysis. The GridMask algorithm is detailed, demonstrating its distinct approach to pixel elimination, enhancing model adaptability to occlusions and local features. A comprehensive dataset of robotic surgical scenarios and instruments is used for evaluation, showcasing the framework's robustness. Specifically, there are improvements of 1.6 percentage points in balanced accuracy for the foreground, 1.7 points in intersection over union (IoU), and 1.7 points in mean Dice similarity coefficient (DSC). These improvements are highly significant and have a substantial impact on inference speed. The inference speed, which is a critical factor in real-time applications, has seen a noteworthy reduction. It decreased from 0.163 milliseconds for the U-Net without GridMask to 0.097 milliseconds for the U-Net with GridMask.