计算机科学
分割
人工智能
变压器
手术器械
利用
计算机视觉
像素
图像分割
工程类
机械工程
计算机安全
电压
电气工程
作者
Nicolás Ayobi,Alejandra Pérez-Rondón,Santiago Rodrı́guez,Pablo Arbeláez
标识
DOI:10.1109/isbi53787.2023.10230819
摘要
We propose Masked-Attention Transformers for Surgical Instrument Segmentation (MATIS), a two-stage, fully transformer-based method that leverages modern pixel-wise attention mechanisms for instrument segmentation. MATIS exploits the instance-level nature of the task by employing a masked attention module that generates and classifies a set of fine instrument region proposals. Our method incorporates long-term video-level information through video transformers to improve temporal consistency and enhance mask classification. We validate our approach in the two standard public benchmarks, Endovis 2017 and Endovis 2018. Our experiments demonstrate that MATIS’ per-frame baseline outperforms previous state-of-the-art methods and that including our temporal consistency module boosts our model’s performance further.
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