MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation
The U-Net architecture is widely recognized as a prominent algorithm for choroidal segmentation in optical coherence tomography (OCT) images. However, conventional U-Net implementations exhibit two critical limitations. First, the backbone employs uniform-sized convolutional kernels to process featu...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10949143/ |
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| author | Dejie Chen Xiangping Chen Hao Gu Su Zhao Hao Jiang |
| author_facet | Dejie Chen Xiangping Chen Hao Gu Su Zhao Hao Jiang |
| author_sort | Dejie Chen |
| collection | DOAJ |
| description | The U-Net architecture is widely recognized as a prominent algorithm for choroidal segmentation in optical coherence tomography (OCT) images. However, conventional U-Net implementations exhibit two critical limitations. First, the backbone employs uniform-sized convolutional kernels to process feature maps across all channels within the same layer, resulting in homogeneous receptive fields and a single-scale bottleneck that impedes global contextual feature extraction. Second, the skip connections are restricted to same-scale feature maps between encoder and decoder, failing to exploit cross-semantic hierarchical feature interactions. To address these issues, this study introduces MSU-Net, a novel neural network for OCT-based choroidal segmentation. The proposed framework enhances performance through two innovations: 1) replacement of standard encoder blocks with a multi-branch module combining heterogeneous convolutions to achieve multi-scale receptive field diversification; 2) redesign of skip connections through a pyramid fusion module with spatial attention for adaptive multi-level feature weighting. This architecture enables progressive refinement of low-level features guided by high-level semantics, significantly improving feature discriminability. Experimental results demonstrate superior performance with metrics of 99.5% (accuracy), 96.7% (sensitivity), 94.7% (Dice), and 94.6% (MIoU), surpassing the baseline by 0.4%, 3.7%, 2.8%, and 2.9% respectively. Notably, the model shows consistent advantages in segmenting indistinct choroidal boundaries compared to state-of-the-art methods. |
| format | Article |
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| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-b7b51caeb72b4de49028133190aab39b2025-08-20T02:29:27ZengIEEEIEEE Access2169-35362025-01-0113706637067510.1109/ACCESS.2025.355780010949143MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image SegmentationDejie Chen0https://orcid.org/0009-0006-0157-9087Xiangping Chen1https://orcid.org/0000-0002-6064-4508Hao Gu2Su Zhao3Hao Jiang4School of Electrical Engineering, Guizhou University, Guiyang, ChinaSchool of Electrical Engineering, Guizhou University, Guiyang, ChinaDepartment of Ophthalmology, The Affiliated Hospital of Guizhou Medical University, Guiyang, ChinaDepartment of Ophthalmology, The Affiliated Hospital of Guizhou Medical University, Guiyang, ChinaDepartment of Ophthalmology, The Affiliated Hospital of Guizhou Medical University, Guiyang, ChinaThe U-Net architecture is widely recognized as a prominent algorithm for choroidal segmentation in optical coherence tomography (OCT) images. However, conventional U-Net implementations exhibit two critical limitations. First, the backbone employs uniform-sized convolutional kernels to process feature maps across all channels within the same layer, resulting in homogeneous receptive fields and a single-scale bottleneck that impedes global contextual feature extraction. Second, the skip connections are restricted to same-scale feature maps between encoder and decoder, failing to exploit cross-semantic hierarchical feature interactions. To address these issues, this study introduces MSU-Net, a novel neural network for OCT-based choroidal segmentation. The proposed framework enhances performance through two innovations: 1) replacement of standard encoder blocks with a multi-branch module combining heterogeneous convolutions to achieve multi-scale receptive field diversification; 2) redesign of skip connections through a pyramid fusion module with spatial attention for adaptive multi-level feature weighting. This architecture enables progressive refinement of low-level features guided by high-level semantics, significantly improving feature discriminability. Experimental results demonstrate superior performance with metrics of 99.5% (accuracy), 96.7% (sensitivity), 94.7% (Dice), and 94.6% (MIoU), surpassing the baseline by 0.4%, 3.7%, 2.8%, and 2.9% respectively. Notably, the model shows consistent advantages in segmenting indistinct choroidal boundaries compared to state-of-the-art methods.https://ieeexplore.ieee.org/document/10949143/Choroidmulti-scale featuresOCT imagereceptive fieldU-Net |
| spellingShingle | Dejie Chen Xiangping Chen Hao Gu Su Zhao Hao Jiang MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation IEEE Access Choroid multi-scale features OCT image receptive field U-Net |
| title | MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation |
| title_full | MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation |
| title_fullStr | MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation |
| title_full_unstemmed | MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation |
| title_short | MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation |
| title_sort | msu net a synthesized u net for exploiting multi scale features in oct image segmentation |
| topic | Choroid multi-scale features OCT image receptive field U-Net |
| url | https://ieeexplore.ieee.org/document/10949143/ |
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