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Collaborative multi-feature extraction and scale-aware semantic information mining for medical image segmentation
Zhang, Ruijun1; He, Zixuan2; Zhu, Jian2; Yuan, Xiaochen3; Huang, Guoheng2; Pun, Chi Man4; Peng, Jianhong5; Lin, Junzhong5; Zhou, Jian6
2022-10-21
Source PublicationPhysics in Medicine and Biology
ISSN0031-9155
Volume67Issue:20
Abstract

Objective. In recent years, methods based on U-shaped structure and skip connection have achieved remarkable results in many medical semantic segmentation tasks. However, the information integration capability of this structure is still limited due to the incompatibility of feature maps of encoding and decoding stages at corresponding levels and lack of extraction of valid information in the final stage of encoding. This structural defect is particularly obvious in segmentation tasks with non-obvious, small and blurred-edge targets. Our objective is to design a novel segmentation network to solve the above problems. Approach. The segmentation network named Global Context-Aware Network is mainly designed by inserting a Multi-feature Collaboration Adaptation (MCA) module, a Scale-Aware Mining (SAM) module and an Edge-enhanced Pixel Intensity Mapping (Edge-PIM) into the U-shaped structure. Firstly, the MCA module can integrate information from all encoding stages and then effectively acts on the decoding stages, solving the problem of information loss during downsampling and pooling. Secondly, the SAM module can further mine information from the encoded high-level features to enrich the information passed to the decoding stage. Thirdly, Edge-PIM can further refine the segmentation results by edge enhancement. Main results. We newly collect Magnetic Resonance Imaging of Colorectal Cancer Liver Metastases (MRI-CRLM) dataset in different imaging sequences with non-obvious, small and blurred-edge liver metastases. Our method performs well on the MRI-CRLM dataset and the publicly available ISIC-2018 dataset, outperforming state-of-the-art methods such as CPFNet on multiple metrics after boxplot analysis, indicating that it can perform well on a wide range of medical image segmentation tasks. Significance. The proposed method solves the problem mentioned above and improved segmentation accuracy for non-obvious, small and blurred-edge targets. Meanwhile, the proposed visualization method Edge-PIM can make the edge more prominent, which can assist medical radiologists in their research work well.

KeywordColorectal Cancer Liver Metastases Edge-enhanced Pixel Intensity Mapping Global Context-aware Network Magnetic Resonance Imaging Multi-feature Collaboration Adaptation Module Scale-aware Mining Module
DOI10.1088/1361-6560/ac95f5
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectEngineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000868166700001
Scopus ID2-s2.0-85140335983
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorZhu, Jian
Affiliation1.College of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou, 510006, China
2.School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China
3.Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao
4.Department of Computer and Information Science, University of Macau, 999078, Macao
5.Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
6.Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
Recommended Citation
GB/T 7714
Zhang, Ruijun,He, Zixuan,Zhu, Jian,et al. Collaborative multi-feature extraction and scale-aware semantic information mining for medical image segmentation[J]. Physics in Medicine and Biology,2022,67(20).
APA Zhang, Ruijun,He, Zixuan,Zhu, Jian,Yuan, Xiaochen,Huang, Guoheng,Pun, Chi Man,Peng, Jianhong,Lin, Junzhong,&Zhou, Jian.(2022).Collaborative multi-feature extraction and scale-aware semantic information mining for medical image segmentation.Physics in Medicine and Biology,67(20).
MLA Zhang, Ruijun,et al."Collaborative multi-feature extraction and scale-aware semantic information mining for medical image segmentation".Physics in Medicine and Biology 67.20(2022).
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