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The aim is to develop a robust model for segmenting surgical instruments against challenging backgrounds.

Accurate Segmentation of Surgical Instruments via Spectral-Attentive Contextual Interaction Network

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The aim is to develop a robust model for segmenting surgical instruments against challenging backgrounds.
Proposed a Spectral-attentive Contextual Interaction Network (SCI-Net) for segmentation.
Introduced Global Context Aggregation Module (GCAM) for coarse localization of instruments.
Developed Spectral-enhanced Feature Module (SFM) for frequency-domain attention.
Designed Scale-aware Dilation Module (SDM) to integrate multi-scale features during decoding.
SCI-Net significantly outperformed other state-of-the-art segmentation methods.
Improved accuracy in segmenting fine boundaries of instruments was observed.
Validated on multiple publicly available surgical instrument segmentation datasets.

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Demonstrates improved segmentation of surgical instruments in robotic systems, suggesting enhanced operational precision.

Mei et al. (2026) studied this question.

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