CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
Multiscale Matters for Part Segmentation of Instruments in Robotic Surgery
wenhao he; Haitao Song; Yue Guo
Source PublicationIET Research Journal
2020
Volume12Issue:12Pages:1-8
Abstract

A challenging aspect of instrument segmentation in robotic surgery is to distinguish different parts of the same instrument.
Parts with similar textures are common in a practical instrument and are difficult to distinguish. In this work, we introduce
an end-to-end recurrent model that comprises a multiscale semantic segmentation network and a refinement model. Specifically,
the semantic segmentation network uniformly transforms the input images in multiple scales into a semantic mask, the refinement
model is a single-scale net recurrently optimizing the above semantic mask. Through extensive experiments, we validate that
the models with multiscale inputs perform better than those to fuse encoding and decoding feature maps with spatial attention.
Furthermore, we verify the effectiveness of our model with state-of-the-art performances on several robotic instrument datasets
derived from MICCAI Endoscopic Vision Challenges.

KeywordMultiscale Matters Part Segmentation Robotic Surgery
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41479
Collection智能制造技术与系统研究中心_多维数据分析
Corresponding AuthorYue Guo
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
wenhao he,Haitao Song,Yue Guo. Multiscale Matters for Part Segmentation of Instruments in Robotic Surgery[J]. IET Research Journal,2020,12(12):1-8.
APA wenhao he,Haitao Song,&Yue Guo.(2020).Multiscale Matters for Part Segmentation of Instruments in Robotic Surgery.IET Research Journal,12(12),1-8.
MLA wenhao he,et al."Multiscale Matters for Part Segmentation of Instruments in Robotic Surgery".IET Research Journal 12.12(2020):1-8.
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