FSE-Net: Rethinking the up-sampling operation in encoder-decoder structure for retinal vessel segmentation
•We present an efficient medical image segmentation framework called FSE-Net for retinal vessel segmentation, which eliminates the up-sampling operation.•To enhance the feature extraction capability in the encoder stage, we introduce the Residual Feature Separable Block (RFSB) module, which effectiv...
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| Published in: | Biomedical signal processing and control Vol. 90; p. 105861 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.04.2024
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| ISSN: | 1746-8094, 1746-8108 |
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| Abstract | •We present an efficient medical image segmentation framework called FSE-Net for retinal vessel segmentation, which eliminates the up-sampling operation.•To enhance the feature extraction capability in the encoder stage, we introduce the Residual Feature Separable Block (RFSB) module, which effectively extracts and refines low-level features, increasing the receptive field of the network.•We introduce the Multi-Head Feature Fusion (MFF) module, which effectively merges low-level and high-level features, streamlining encoder-decoder structure.
Automatic retinal vessel segmentation plays a crucial role in the diagnosis and assessment of various ophthalmologic diseases. Currently, the primary retinal vessel segmentation algorithms are based on the encoder-decoder structure. However, these U-Net analogs suffer from the loss of both spatial and semantic information, caused by continuous up-sampling operations in the decoder structure. In this paper, we rethink the above problem and build a novel deep neural network for retinal vessel segmentation, called FSE-Net. Specifically, to address the issue of feature information loss and enhance the performance of retinal vessel segmentation, we eliminate the decoder structure. In particular, we introduced a multi-head feature fusion block (MFF) as a substitute for the continuous up-sampling operation. Additionally, the encoder stage of FSE-Net incorporates a residual feature separable block (RFSB) to further refine and distill features, thereby enhancing the capability of feature extraction. Subsequently, we employ a residual atrous spatial feature aggregate module (RASF) to expand the network's receptive field by incorporating multi-scale feature information. We conducted experiments on five widely recognized databases for retinal vessel segmentation, namely DRIVE, CHASEDB1, STARE, IOSTAR, and LES-AV. The results demonstrate that our proposed FSE-Net outperforms state-of-the-art approaches in terms of segmentation performance. Moreover, we demonstrate the feasibility of achieving superior segmentation performance without employing the traditional U-Net analog network structure. |
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| AbstractList | •We present an efficient medical image segmentation framework called FSE-Net for retinal vessel segmentation, which eliminates the up-sampling operation.•To enhance the feature extraction capability in the encoder stage, we introduce the Residual Feature Separable Block (RFSB) module, which effectively extracts and refines low-level features, increasing the receptive field of the network.•We introduce the Multi-Head Feature Fusion (MFF) module, which effectively merges low-level and high-level features, streamlining encoder-decoder structure.
Automatic retinal vessel segmentation plays a crucial role in the diagnosis and assessment of various ophthalmologic diseases. Currently, the primary retinal vessel segmentation algorithms are based on the encoder-decoder structure. However, these U-Net analogs suffer from the loss of both spatial and semantic information, caused by continuous up-sampling operations in the decoder structure. In this paper, we rethink the above problem and build a novel deep neural network for retinal vessel segmentation, called FSE-Net. Specifically, to address the issue of feature information loss and enhance the performance of retinal vessel segmentation, we eliminate the decoder structure. In particular, we introduced a multi-head feature fusion block (MFF) as a substitute for the continuous up-sampling operation. Additionally, the encoder stage of FSE-Net incorporates a residual feature separable block (RFSB) to further refine and distill features, thereby enhancing the capability of feature extraction. Subsequently, we employ a residual atrous spatial feature aggregate module (RASF) to expand the network's receptive field by incorporating multi-scale feature information. We conducted experiments on five widely recognized databases for retinal vessel segmentation, namely DRIVE, CHASEDB1, STARE, IOSTAR, and LES-AV. The results demonstrate that our proposed FSE-Net outperforms state-of-the-art approaches in terms of segmentation performance. Moreover, we demonstrate the feasibility of achieving superior segmentation performance without employing the traditional U-Net analog network structure. |
| ArticleNumber | 105861 |
| Author | Pan, An Mu, Wei Ni, Jiajia Chen, Zhengming |
| Author_xml | – sequence: 1 givenname: Jiajia orcidid: 0000-0002-0158-0890 surname: Ni fullname: Ni, Jiajia organization: School of Artificial Intelligence, Anhui Polytechnic University, Wuhu, China – sequence: 2 givenname: Wei surname: Mu fullname: Mu, Wei organization: College of Internet of Things Engineering, HoHai University, Changzhou, China – sequence: 3 givenname: An surname: Pan fullname: Pan, An organization: College of Internet of Things Engineering, HoHai University, Changzhou, China – sequence: 4 givenname: Zhengming surname: Chen fullname: Chen, Zhengming organization: College of Internet of Things Engineering, HoHai University, Changzhou, China |
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| Keywords | Retinal vessel segmentation Feature fusion Convolutional neural network U-Net |
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| Title | FSE-Net: Rethinking the up-sampling operation in encoder-decoder structure for retinal vessel segmentation |
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