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1. Robot-assisted minimally invasive surgery (RMIS) has advantages over traditional open surgeries, such as less pain and quicker recovery. However, the narrow field of view and intensive workload for surgeons in RMIS make automatic context awareness crucial for improving surgeon performance and patient safety.

2. Automatic segmentation of surgical instruments is challenging due to factors like motion blur, specular reflection, tissue occlusion, and limited endoscopic view. Previous methods using hand-crafted features and machine learning classifiers were ineffective in distinguishing specific parts and types of instruments.

3. Deep learning techniques, particularly convolutional neural networks (CNNs), have shown promise in accurate segmentation of surgical instruments. The proposed model in this study combines the Mask R-CNN framework with anchor optimization and an improved Region Proposal Network (RPN) to achieve accurate instance segmentation of surgical instruments. Cross-dataset evaluation demonstrates the model's generalization capability and its potential for improving performance on in-house datasets.

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