Surgical instrument segmentation for endoscopic vision with data fusion of CNN prediction and kinematic pose
Fangbo Qin1,2; Yangming Li3; Yun-Hsuan Su4; De Xu1,2; Blake Hannaford4
2019-08
会议名称2019 International Conference on Robotics and Automation (ICRA)
会议日期2019-5-20
会议地点Montreal, Canada
出版者IEEE
摘要

The real-time and robust surgical instrument segmentation is an important issue for endoscopic vision. We propose an instrument segmentation method fusing the convolutional neural networks (CNN) prediction and the kinematic pose information. First, the CNN model ToolNet-C is designed, which cascades a convolutional feature extractor trained over numerous unlabeled images and a pixel-wise segmentor trained on few labeled images. Second, the silhouette projection of the instrument body onto the endoscopic image is implemented based on the measured kinematic pose. Third, the particle filter with the shape matching likelihood and the weight suppression is proposed for data fusion, whose estimate refines the kinematic pose. The refined pose determines an accurate silhouette mask, which is the final segmentation output. The experiments are conducted with a surgical navigation system, several animal-tissue backgrounds, and a debrider instrument.

DOI10.1109/ICRA.2019.8794122
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收录类别EI
语种英语
七大方向——子方向分类医学影像处理与分析
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被引频次:28[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/25772
专题精密感知与控制研究中心_精密感知与控制
通讯作者Blake Hannaford
作者单位1.中国科学院自动化研究所
2.中国科学院大学
3.Rochester Institute of Technology, Rochester, NY 14623, USA.
4.University of Washington, Seattle, WA 98195-2500, USA
第一作者单位中国科学院自动化研究所
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Fangbo Qin,Yangming Li,Yun-Hsuan Su,et al. Surgical instrument segmentation for endoscopic vision with data fusion of CNN prediction and kinematic pose[C]:IEEE,2019.
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