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Multi-scale contextual semantic enhancement network for 3D medical image segmentation
Xia, Tingjian1; Huang, Guoheng1; Pun, Chi Man2; Zhang, Weiwen1; Li, Jiajian1; Ling, Wing Kuen3; Lin, Chao4; Yang, Qi4
2022-11-21
Source PublicationPhysics in Medicine and Biology
ISSN0031-9155
Volume67Issue:22
Abstract

Objective. Accurate and automatic segmentation of medical images is crucial for improving the efficiency of disease diagnosis and making treatment plans. Although methods based on convolutional neural networks have achieved excellent results in numerous segmentation tasks of medical images, they still suffer from challenges including drastic scale variations of lesions, blurred boundaries of lesions and class imbalance. Our objective is to design a segmentation framework named multi-scale contextual semantic enhancement network (3D MCSE-Net) to address the above problems. Approach. The 3D MCSE-Net mainly consists of a multi-scale context pyramid fusion module (MCPFM), a triple feature adaptive enhancement module (TFAEM), and an asymmetric class correction loss (ACCL) function. Specifically, the MCPFM resolves the problem of unreliable predictions due to variable morphology and drastic scale variations of lesions by capturing the multi-scale global context of feature maps. Subsequently, the TFAEM overcomes the problem of blurred boundaries of lesions caused by the infiltrating growth and complex context of lesions by adaptively recalibrating and enhancing the multi-dimensional feature representation of suspicious regions. Moreover, the ACCL alleviates class imbalances by adjusting asy mmetric correction coefficient and weighting factor. Main results. Our method is evaluated on the nasopharyngeal cancer tumor segmentation (NPCTS) dataset, the public dataset of the MICCAI 2017 liver tumor segmentation (LiTS) challenge and the 3D image reconstruction for comparison of algorithm and DataBase (3Dircadb) dataset to verify its effectiveness and generalizability. The experimental results show the proposed components all have unique strengths and exhibit mutually reinforcing properties. More importantly, the proposed 3D MCSE-Net outperforms previous state-of-the-art methods for tumor segmentation on the NPCTS, LiTS and 3Dircadb dataset. Significance. Our method addresses the effects of drastic scale variations of lesions, blurred boundaries of lesions and class imbalance, and improves tumors segmentation accuracy, which facilitates clinical medical diagnosis and treatment planning.

Keyword3d Medical Image Segmentation Class Imbalance Feature Enhancement Liver Tumor Multi-scale Context Nasopharyngeal Carcinoma
DOI10.1088/1361-6560/ac9e41
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:000886040200001
Scopus ID2-s2.0-85142448063
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China
2.Department of Computer and Information Science, University of Macau, Macau, 999078, Macao
3.School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China
4.Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
Recommended Citation
GB/T 7714
Xia, Tingjian,Huang, Guoheng,Pun, Chi Man,et al. Multi-scale contextual semantic enhancement network for 3D medical image segmentation[J]. Physics in Medicine and Biology,2022,67(22).
APA Xia, Tingjian,Huang, Guoheng,Pun, Chi Man,Zhang, Weiwen,Li, Jiajian,Ling, Wing Kuen,Lin, Chao,&Yang, Qi.(2022).Multi-scale contextual semantic enhancement network for 3D medical image segmentation.Physics in Medicine and Biology,67(22).
MLA Xia, Tingjian,et al."Multi-scale contextual semantic enhancement network for 3D medical image segmentation".Physics in Medicine and Biology 67.22(2022).
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