Rethinking the Encoder–decoder Structure in Medical Image Segmentation from Releasing Decoder Structure
Medical image segmentation has witnessed rapid advancements with the emergence of encoder–decoder based methods. In the encoder–decoder structure, the primary goal of the decoding phase is not only to restore feature map resolution, but also to mitigate the loss of feature information incurred durin...
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| Vydáno v: | Journal of bionics engineering Ročník 21; číslo 3; s. 1511 - 1521 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Springer Nature Singapore
01.05.2024
Springer Nature B.V |
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| ISSN: | 1672-6529, 2543-2141 |
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| Abstract | Medical image segmentation has witnessed rapid advancements with the emergence of encoder–decoder based methods. In the encoder–decoder structure, the primary goal of the decoding phase is not only to restore feature map resolution, but also to mitigate the loss of feature information incurred during the encoding phase. However, this approach gives rise to a challenge: multiple up-sampling operations in the decoder segment result in the loss of feature information. To address this challenge, we propose a novel network that removes the decoding structure to reduce feature information loss (CBL-Net). In particular, we introduce a Parallel Pooling Module (PPM) to counteract the feature information loss stemming from conventional and pooling operations during the encoding stage. Furthermore, we incorporate a Multiplexed Dilation Convolution (MDC) module to expand the network's receptive field. Also, although we have removed the decoding stage, we still need to recover the feature map resolution. Therefore, we introduced the Global Feature Recovery (GFR) module. It uses attention mechanism for the image feature map resolution recovery, which can effectively reduce the loss of feature information. We conduct extensive experimental evaluations on three publicly available medical image segmentation datasets: DRIVE, CHASEDB and MoNuSeg datasets. Experimental results show that our proposed network outperforms state-of-the-art methods in medical image segmentation. In addition, it achieves higher efficiency than the current network of coding and decoding structures by eliminating the decoding component. |
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| AbstractList | Medical image segmentation has witnessed rapid advancements with the emergence of encoder–decoder based methods. In the encoder–decoder structure, the primary goal of the decoding phase is not only to restore feature map resolution, but also to mitigate the loss of feature information incurred during the encoding phase. However, this approach gives rise to a challenge: multiple up-sampling operations in the decoder segment result in the loss of feature information. To address this challenge, we propose a novel network that removes the decoding structure to reduce feature information loss (CBL-Net). In particular, we introduce a Parallel Pooling Module (PPM) to counteract the feature information loss stemming from conventional and pooling operations during the encoding stage. Furthermore, we incorporate a Multiplexed Dilation Convolution (MDC) module to expand the network's receptive field. Also, although we have removed the decoding stage, we still need to recover the feature map resolution. Therefore, we introduced the Global Feature Recovery (GFR) module. It uses attention mechanism for the image feature map resolution recovery, which can effectively reduce the loss of feature information. We conduct extensive experimental evaluations on three publicly available medical image segmentation datasets: DRIVE, CHASEDB and MoNuSeg datasets. Experimental results show that our proposed network outperforms state-of-the-art methods in medical image segmentation. In addition, it achieves higher efficiency than the current network of coding and decoding structures by eliminating the decoding component. |
| Author | Pan, An Mu, Wei Ni, Jiajia Chen, Zhengming |
| Author_xml | – sequence: 1 givenname: Jiajia orcidid: 0000-0002-0066-4689 surname: Ni fullname: Ni, Jiajia email: jj.ni@ahpu.edu.cn organization: School of Artificial Intelligence, Anhui Polytechnic University, School of Information Science and Engineering, HoHai University – sequence: 2 givenname: Wei surname: Mu fullname: Mu, Wei organization: School of Information Science and Engineering, HoHai University – sequence: 3 givenname: An surname: Pan fullname: Pan, An organization: School of Information Science and Engineering, HoHai University – sequence: 4 givenname: Zhengming surname: Chen fullname: Chen, Zhengming organization: School of Information Science and Engineering, HoHai University |
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| ContentType | Journal Article |
| Copyright | Jilin University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Jilin University 2024. |
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| Keywords | Attention mechanisms Encoder–decoder architecture Releasing decoder architecture Neural network Medical image segmentation |
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| SubjectTerms | Artificial Intelligence Biochemical Engineering Bioinformatics Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Coders Coding Datasets Decoding Engineering Feature maps Image processing Image segmentation Medical imaging Modules Receptive field Recovery Research Article |
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| Title | Rethinking the Encoder–decoder Structure in Medical Image Segmentation from Releasing Decoder Structure |
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| Volume | 21 |
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