MASSD: Multi-scale attention single shot detector for surgical instruments.

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Abstract

Surgical instrument detection is a significant task in computer-aided minimal invasive surgery for providing real-time feedback to physicians, evaluating surgical skills, and developing a training plan for surgeons. In this study, a multi-scale attention single detector is designed for surgical instruments. In the field of object detection, accurate detection of small objects is always a challenging task. We propose an innovative feature fusion technique aimed at small surgical instrument detection. First, the attention map is created from high-level features to act on the low-level features and enrich the semantic information of the low-level features. The original and processed features are then fused by skip connection. Finally, multi-scale feature maps are created to predict fusion features. The experiments on the ATLAS Dione dataset yielded results with a detection time of 0.066 s per frame and a mean average precision of 90.08%. Our proposed feature fusion module can obtain more semantic information for low-level features and significantly enhance the performance of small surgical instrument detection.
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