CFFR-Net: A channel-wise features fusion and recalibration network for surgical instruments segmentation

Surgical instrument segmentation plays a crucial role in robot-assisted surgery by furnishing essential information about instrument location and orientation. This information not only enhances surgical planning but also augments the precision and safety of procedures. Despite promising strides in recent research on surgical instrument segmentation, accuracy still faces obstacles due to local feature processing limitations, surgical environment complexity, and instrument morphological variability. To address these challenges, we introduced the channel-wise features fusion and recalibration network (CFFR-Net). This network utilizes a dual-stream mechanism, combining a context-guided block and dense block for feature extraction. The context-guided block captures a variety of contextual information by using different dilation rates. Additionally, CFFR-Net employs a fusion mechanism that harmonizes context-guided and dense streams. This integration, along with the inclusion of Squeeze-and-Excitation attention, enhances both the precision and robustness of semantic instrument segmentation.