Boundary-aware context neural network for medical image segmentation

•Propose a boundary-aware context neural network for 2D medical image segmentation.•The pyramid edge extraction module aggregates edge information with multigranularity.•The multi-task learning module enriches the context by the different task branches.•The cross feature fusion module aims to select...

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Published in:	Medical image analysis Vol. 78; p. 102395
Main Authors:	Wang, Ruxin, Chen, Shuyuan, Ji, Chaojie, Fan, Jianping, Li, Ye
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01.05.2022 Elsevier BV
Subjects:	Artificial neural networks Boundaries Coders Complementarity Context Convolutional neural network Deep learning Encoders-Decoders Feature extraction Feature maps Humans Image processing Image Processing, Computer-Assisted - methods Image Processing, Computer-Assisted - standards Image segmentation Learning Masks Medical image segmentation Medical imaging Modules Neural networks Neural Networks, Computer Production methods Spatial data Deep learning Convolutional neural network Medical image segmentation
ISSN:	1361-8415, 1361-8423, 1361-8423
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Abstract	•Propose a boundary-aware context neural network for 2D medical image segmentation.•The pyramid edge extraction module aggregates edge information with multigranularity.•The multi-task learning module enriches the context by the different task branches.•The cross feature fusion module aims to selectively aggregate multi-level features.•Achieving state-of-the-art performances on five medical image segmentation datasets. [Display omitted] Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. With the development of convolutional neural networks (CNNs), medical image segmentation performance has advanced significantly. However, most existing CNN-based methods often produce unsatisfactory segmentation masks without accurate object boundaries. This problem is caused by the limited context information and inadequate discriminative feature maps after consecutive pooling and convolution operations. Additionally, medical images are characterized by high intra-class variation, inter-class indistinction and noise, extracting powerful context and aggregating discriminative features for fine-grained segmentation remain challenging. In this study, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information, which incorporates encoder-decoder architecture. In each stage of the encoder sub-network, a proposed pyramid edge extraction module first obtains multi-granularity edge information. Then a newly designed mini multi-task learning module for jointly learning segments the object masks and detects lesion boundaries, in which a new interactive attention layer is introduced to bridge the two tasks. In this way, information complementarity between different tasks is achieved, which effectively leverages the boundary information to offer strong cues for better segmentation prediction. Finally, a cross feature fusion module acts to selectively aggregate multi-level features from the entire encoder sub-network. By cascading these three modules, richer context and fine-grain features of each stage are encoded and then delivered to the decoder. The results of extensive experiments on five datasets show that the proposed BA-Net outperforms state-of-the-art techniques.
AbstractList	Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. With the development of convolutional neural networks (CNNs), medical image segmentation performance has advanced significantly. However, most existing CNN-based methods often produce unsatisfactory segmentation masks without accurate object boundaries. This problem is caused by the limited context information and inadequate discriminative feature maps after consecutive pooling and convolution operations. Additionally, medical images are characterized by high intra-class variation, inter-class indistinction and noise, extracting powerful context and aggregating discriminative features for fine-grained segmentation remain challenging. In this study, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information, which incorporates encoder-decoder architecture. In each stage of the encoder sub-network, a proposed pyramid edge extraction module first obtains multi-granularity edge information. Then a newly designed mini multi-task learning module for jointly learning segments the object masks and detects lesion boundaries, in which a new interactive attention layer is introduced to bridge the two tasks. In this way, information complementarity between different tasks is achieved, which effectively leverages the boundary information to offer strong cues for better segmentation prediction. Finally, a cross feature fusion module acts to selectively aggregate multi-level features from the entire encoder sub-network. By cascading these three modules, richer context and fine-grain features of each stage are encoded and then delivered to the decoder. The results of extensive experiments on five datasets show that the proposed BA-Net outperforms state-of-the-art techniques.Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. With the development of convolutional neural networks (CNNs), medical image segmentation performance has advanced significantly. However, most existing CNN-based methods often produce unsatisfactory segmentation masks without accurate object boundaries. This problem is caused by the limited context information and inadequate discriminative feature maps after consecutive pooling and convolution operations. Additionally, medical images are characterized by high intra-class variation, inter-class indistinction and noise, extracting powerful context and aggregating discriminative features for fine-grained segmentation remain challenging. In this study, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information, which incorporates encoder-decoder architecture. In each stage of the encoder sub-network, a proposed pyramid edge extraction module first obtains multi-granularity edge information. Then a newly designed mini multi-task learning module for jointly learning segments the object masks and detects lesion boundaries, in which a new interactive attention layer is introduced to bridge the two tasks. In this way, information complementarity between different tasks is achieved, which effectively leverages the boundary information to offer strong cues for better segmentation prediction. Finally, a cross feature fusion module acts to selectively aggregate multi-level features from the entire encoder sub-network. By cascading these three modules, richer context and fine-grain features of each stage are encoded and then delivered to the decoder. The results of extensive experiments on five datasets show that the proposed BA-Net outperforms state-of-the-art techniques. Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. With the development of convolutional neural networks (CNNs), medical image segmentation performance has advanced significantly. However, most existing CNN-based methods often produce unsatisfactory segmentation masks without accurate object boundaries. This problem is caused by the limited context information and inadequate discriminative feature maps after consecutive pooling and convolution operations. Additionally, medical images are characterized by high intra-class variation, inter-class indistinction and noise, extracting powerful context and aggregating discriminative features for fine-grained segmentation remain challenging. In this study, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information, which incorporates encoder-decoder architecture. In each stage of the encoder sub-network, a proposed pyramid edge extraction module first obtains multi-granularity edge information. Then a newly designed mini multi-task learning module for jointly learning segments the object masks and detects lesion boundaries, in which a new interactive attention layer is introduced to bridge the two tasks. In this way, information complementarity between different tasks is achieved, which effectively leverages the boundary information to offer strong cues for better segmentation prediction. Finally, a cross feature fusion module acts to selectively aggregate multi-level features from the entire encoder sub-network. By cascading these three modules, richer context and fine-grain features of each stage are encoded and then delivered to the decoder. The results of extensive experiments on five datasets show that the proposed BA-Net outperforms state-of-the-art techniques. •Propose a boundary-aware context neural network for 2D medical image segmentation.•The pyramid edge extraction module aggregates edge information with multigranularity.•The multi-task learning module enriches the context by the different task branches.•The cross feature fusion module aims to selectively aggregate multi-level features.•Achieving state-of-the-art performances on five medical image segmentation datasets. [Display omitted] Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. With the development of convolutional neural networks (CNNs), medical image segmentation performance has advanced significantly. However, most existing CNN-based methods often produce unsatisfactory segmentation masks without accurate object boundaries. This problem is caused by the limited context information and inadequate discriminative feature maps after consecutive pooling and convolution operations. Additionally, medical images are characterized by high intra-class variation, inter-class indistinction and noise, extracting powerful context and aggregating discriminative features for fine-grained segmentation remain challenging. In this study, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information, which incorporates encoder-decoder architecture. In each stage of the encoder sub-network, a proposed pyramid edge extraction module first obtains multi-granularity edge information. Then a newly designed mini multi-task learning module for jointly learning segments the object masks and detects lesion boundaries, in which a new interactive attention layer is introduced to bridge the two tasks. In this way, information complementarity between different tasks is achieved, which effectively leverages the boundary information to offer strong cues for better segmentation prediction. Finally, a cross feature fusion module acts to selectively aggregate multi-level features from the entire encoder sub-network. By cascading these three modules, richer context and fine-grain features of each stage are encoded and then delivered to the decoder. The results of extensive experiments on five datasets show that the proposed BA-Net outperforms state-of-the-art techniques.
ArticleNumber	102395
Author	Ji, Chaojie Li, Ye Chen, Shuyuan Wang, Ruxin Fan, Jianping
Author_xml	– sequence: 1 givenname: Ruxin orcidid: 0000-0003-4772-3284 surname: Wang fullname: Wang, Ruxin email: rx.wang@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China – sequence: 2 givenname: Shuyuan surname: Chen fullname: Chen, Shuyuan email: sa517020@mail.ustc.edu.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China – sequence: 3 givenname: Chaojie orcidid: 0000-0001-5502-7508 surname: Ji fullname: Ji, Chaojie email: cj.ji@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China – sequence: 4 givenname: Jianping surname: Fan fullname: Fan, Jianping email: jp.fan@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China – sequence: 5 givenname: Ye surname: Li fullname: Li, Ye email: ye.li@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/35231851$$D View this record in MEDLINE/PubMed
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Snippet	•Propose a boundary-aware context neural network for 2D medical image segmentation.•The pyramid edge extraction module aggregates edge information with... Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. With the development of convolutional neural...
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SubjectTerms	Artificial neural networks Boundaries Coders Complementarity Context Convolutional neural network Deep learning Encoders-Decoders Feature extraction Feature maps Humans Image processing Image Processing, Computer-Assisted - methods Image Processing, Computer-Assisted - standards Image segmentation Learning Masks Medical image segmentation Medical imaging Modules Neural networks Neural Networks, Computer Production methods Spatial data
Title	Boundary-aware context neural network for medical image segmentation
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