CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
DFR-Net: A Novel Multi-Task Learning Network for Real-Time Multi-Instrument Segmentation
Zhou, Yan-Jie1,2; Liu, Shi-Qi1; Xie, Xiao-Liang1,2; Hou, Zeng-Guang1,2,3,4
2021-10
Conference NameACM International Conference on Multimedia (ACM MM)
Conference Date2021.10.20-24
Conference Place中国成都
PublisherACM
Abstract

In computer-assisted vascular surgery, real-time multi-instrument segmentation serves as a pre-requisite step. However, a large amount of effort has been dedicated to single-instrument rather than multi-instrument in computer-assisted intervention research to this day. To fill the overlooked gap, this study introduces a Light-Weight Deep Feature Refinement Network (DFR-Net) based on multi-task learning for real-time multi-instrument segmentation. In this network, the proposed feature refinement module (FRM) can capture long-term dependencies while retaining precise positional information, which helps the model locate the foreground objects of interest. The designed channel calibration module (CCM) can re-calibrate fusion weights of multi-level features, which helps the model balance the importance of semantic information and appearance information. Besides, the connectivity loss function is developed to address fractures in the wire-like structure segmentation results. Extensive experiments on two different types of datasets consistently demonstrate that DFR-Net can achieve state-of-the-art segmentation performance while meeting the real-time requirements.

Keywordneural networks instrument segmentation multi-task learning
Indexed ByEI
Funding ProjectNational Natural Science Foundation of China[U1613210] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32040000] ; National Natural Science Foundation of China[61533016] ; National Natural Science Foundation of China[61533016] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32040000] ; National Natural Science Foundation of China[U1613210]
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48544
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorHou, Zeng-Guang
Affiliation1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, CAS
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
3.Center for Excellence in Brain Science and Intelligence Technology
4.CASIA-MUST Joint Laboratory of Intelligence Science and Technology, MUST
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhou, Yan-Jie,Liu, Shi-Qi,Xie, Xiao-Liang,et al. DFR-Net: A Novel Multi-Task Learning Network for Real-Time Multi-Instrument Segmentation[C]:ACM,2021.
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