CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Space Squeeze Reasoning and Low-Rank Bilinear Feature Fusion for Surgical Image Segmentation
Zhen-Liang Ni1,2; Gui-Bin Bian1,2; Zhen Li1; Xiao-Hu Zhou1; Rui-Qi Li1,2; Zeng-Guang Hou1,2,3,4
Source PublicationIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
2022-02-28
Pages1
Subtype期刊论文
Abstract

Surgical image segmentation is critical for surgical robot control and computer-assisted surgery. In the surgical scene, the local features of objects are highly similar, and the illumination interference is strong, which makes surgical image segmentation challenging. To address the above issues, a bilinear squeeze reasoning network is proposed for surgical image segmentation. In it, the space squeeze reasoning module is proposed, which adopts height pooling and width pooling to squeeze global contexts in the vertical and horizontal directions, respectively.
The similarity between each horizontal position and each vertical position is calculated to encode long-range semantic dependencies and establish the affinity matrix. The feature maps are also squeezed from both the vertical and horizontal directions to model channel relations. Guided by channel relations, the affinity matrix is expanded to the same size as the input features. It captures longrange semantic dependencies from different directions, helping address the local similarity issue. Besides, a lowrank bilinear fusion module is proposed to enhance the model’s ability to recognize similar features. This module is based on the low-rank bilinear model to capture the inter-layer feature relations. It integrates the location details from low-level features and semantic information from highlevel features. Various semantics can be represented more accurately, which effectively improves feature representation. The proposed network achieves state-of-the-art performance on cataract image segmentation dataset CataSeg and robotic image segmentation dataset EndoVis 2018.

KeywordSurgical Image Segmentation Space Squeeze Reasoning Bilinear Feature Fusion
DOI10.1109/JBHI.2022.3154925
Indexed BySCI
Language英语
WOS IDWOS:000819832600036
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48682
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorGui-Bin Bian; Zeng-Guang Hou
Affiliation1.the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.the School of Artificial Intelligence, University of Chinese Academy of Sciences
3.the CAS Center for Excellence in Brain Science and Technology
4.the CAS Center for Excellence in Brain Science and Technology
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhen-Liang Ni,Gui-Bin Bian,Zhen Li,et al. Space Squeeze Reasoning and Low-Rank Bilinear Feature Fusion for Surgical Image Segmentation[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2022:1.
APA Zhen-Liang Ni,Gui-Bin Bian,Zhen Li,Xiao-Hu Zhou,Rui-Qi Li,&Zeng-Guang Hou.(2022).Space Squeeze Reasoning and Low-Rank Bilinear Feature Fusion for Surgical Image Segmentation.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,1.
MLA Zhen-Liang Ni,et al."Space Squeeze Reasoning and Low-Rank Bilinear Feature Fusion for Surgical Image Segmentation".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022):1.
Files in This Item: Download All
File Name/Size DocType Version Access License
JBHI.pdf(3263KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhen-Liang Ni]'s Articles
[Gui-Bin Bian]'s Articles
[Zhen Li]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhen-Liang Ni]'s Articles
[Gui-Bin Bian]'s Articles
[Zhen Li]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhen-Liang Ni]'s Articles
[Gui-Bin Bian]'s Articles
[Zhen Li]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: JBHI.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.