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arXiv · 2011.08752

Multi-frame Feature Aggregation for Real-time Instrument Segmentation in Endoscopic Video

Shan Lin·Fangbo Qin·Haonan Peng·Randall A. Bly·Kris S. Moe·Blake Hannaford
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Abstract

Deep learning-based methods have achieved promising results on surgical instrument segmentation. However, the high computation cost may limit the application of deep models to time-sensitive tasks such as online surgical video analysis for robotic-assisted surgery. Moreover, current methods may still suffer from challenging conditions in surgical images such as various lighting conditions and the presence of blood. We propose a novel Multi-frame Feature Aggregation (MFFA) module to aggregate video frame features temporally and spatially in a recurrent mode. By distributing the computation load of deep feature extraction over sequential frames, we can use a lightweight encoder to reduce the computation costs at each time step. Moreover, public surgical videos usually are not labeled frame by frame, so we develop a method that can randomly synthesize a surgical frame sequence from a single labeled frame to assist network training. We demonstrate that our approach achieves superior performance to corresponding deeper segmentation models on two public surgery datasets.

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Shan Lin, Fangbo Qin, Haonan Peng, Randall A. Bly, Kris S. Moe, Blake Hannaford. 2020-11-17. Multi-frame Feature Aggregation for Real-time Instrument Segmentation in Endoscopic Video. https://doi.org/10.1109/lra.2021.3096156

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