3D vessel-like structure segmentation in medical images by an edge-reinforced network
The vessel-like structure in biomedical images, such as within cerebrovascular and nervous pathologies, is an essential biomarker in understanding diseases’ mechanisms and in diagnosing and treating diseases. However, existing vessel-like structure segmentation methods often produce unsatisfactory results due to challenging segmentations for crisp edges. The edge and nonedge voxels of the vessel-like structure in three-dimensional (3D) medical images usually have a highly imbalanced distribution as most voxels are non-edge, making it challenging to find crisp edges. In this work, we propose a generic neural network for the segmentation of the vessel-like structures in different 3D medical imaging modalities. The new edge-reinforced neural network (ER-Net) is based on an encoder–decoder architecture. Moreover, a reverse edge attention module and an edge-reinforced optimization loss are proposed to increase the weight of the voxels on the edge of the given 3D volume to discover and better preserve the spatial edge information. A feature selection module is further introduced to select discriminative features adaptively from an encoder and decoder simultaneously, which aims to increase the weight of edge voxels, thus significantly improving the segmentation performance. The proposed method is thoroughly validated using four publicly accessible datasets, and the experimental results demonstrate that the proposed method generally outperforms other state-of-the-art algorithms for various metrics.
Natural Science Foundation of Ningbo[202003N4039];Natural Science Foundation of Ningbo[202003N4040];Youth Innovation Promotion Association of the Chinese Academy of Sciences;Natural Science Foundation of Beijing Municipality;National Natural Science Foundation of China;National Natural Science Foundation of China;Natural Science Foundation of Zhejiang Province[LR22F020008];Natural Science Foundation of Zhejiang Province[LZ19F010001];
|WOS Research Area|
Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
|WOS Accession No|
|EI Accession Number|
Decoding ; Diagnosis ; Image segmentation ; Reinforcement ; Signal encoding
|ESI Classification Code|
Biomedical Engineering:461.1 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Imaging Techniques:746 ; Materials Science:951
|ESI Research Field|
Cited Times [WOS]:3
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
1.College of Information Engineering,Capital Normal University,Beijing,China
2.Cixi Institute of Biomedical Engineering,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo,China
3.The Affiliated People's Hospital of Ningbo University,Ningbo,China
4.School of Control Science and Engineering,Shandong University,Jinan,China
5.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
6.Department of Computer Science,Winona State University,Winona,United States
Xia，Likun,Zhang，Hao,Wu，Yufei,et al. 3D vessel-like structure segmentation in medical images by an edge-reinforced network[J]. MEDICAL IMAGE ANALYSIS,2022,82.
Xia，Likun.,Zhang，Hao.,Wu，Yufei.,Song，Ran.,Ma，Yuhui.,...&Zhao，Yitian.(2022).3D vessel-like structure segmentation in medical images by an edge-reinforced network.MEDICAL IMAGE ANALYSIS,82.
Xia，Likun,et al."3D vessel-like structure segmentation in medical images by an edge-reinforced network".MEDICAL IMAGE ANALYSIS 82(2022).
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