D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images
Gathering detailed morphological information from retinal blood vessels is crucial in clinical diagnostics, enabling doctors to make precise assessments of patient conditions and to devise custom treatments. Traditional methods of segmenting these vessels from fundus images are not only tedious but...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10855415/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832575599741239296 |
|---|---|
| author | Vo Trong Quang Huy Chih-Min Lin |
| author_facet | Vo Trong Quang Huy Chih-Min Lin |
| author_sort | Vo Trong Quang Huy |
| collection | DOAJ |
| description | Gathering detailed morphological information from retinal blood vessels is crucial in clinical diagnostics, enabling doctors to make precise assessments of patient conditions and to devise custom treatments. Traditional methods of segmenting these vessels from fundus images are not only tedious but require a high degree of specialized knowledge. In light of this, Deep Convolutional Neural Networks (DCNNs), particularly those based on the U-Net architecture, have been acknowledged for their effectiveness in capturing and utilizing contextual features within this context. However, these methods often grapple with challenges such as loss of vital information during pooling and insufficient handling of local context in skip connections, leading to less than optimal results. To address these limitations, this research propounds the novel Convolution Block Dual Attention Module (CBDAM), as well as two pioneering network architectures: The Convolution Block Dual Attention Module Unet (CBDAMUNet) and the Dual Decoder Convolution Block Attention Module with Attention U-Net (D2CBDAMAttUnet). These are built upon a fortified encoder-decoder structure aimed at providing an automated, streamlined detection mechanism from fundus imagery. Thoroughly tested against the DRIVE, CHASEDB1 and STARE datasets, recognized standards in retinal vessel segmentation, the proposed models not only redefine the accuracy benchmarks but also represent a significant stride in automated retinal vessel analysis. The introduction of these advanced networks is a milestone in ophthalmologic diagnostics and research, offering a potent asset for medical professionals and specialists in the field. |
| format | Article |
| id | doaj-art-8ebfb693068e4de1b7debd3afa164e06 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8ebfb693068e4de1b7debd3afa164e062025-01-31T23:04:50ZengIEEEIEEE Access2169-35362025-01-0113196351964910.1109/ACCESS.2025.353523910855415D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus ImagesVo Trong Quang Huy0https://orcid.org/0009-0003-7062-5671Chih-Min Lin1https://orcid.org/0000-0003-2107-5012Department of Electrical Engineering, Yuan Ze University, Taoyuan, TaiwanDepartment of Electrical Engineering, Yuan Ze University, Taoyuan, TaiwanGathering detailed morphological information from retinal blood vessels is crucial in clinical diagnostics, enabling doctors to make precise assessments of patient conditions and to devise custom treatments. Traditional methods of segmenting these vessels from fundus images are not only tedious but require a high degree of specialized knowledge. In light of this, Deep Convolutional Neural Networks (DCNNs), particularly those based on the U-Net architecture, have been acknowledged for their effectiveness in capturing and utilizing contextual features within this context. However, these methods often grapple with challenges such as loss of vital information during pooling and insufficient handling of local context in skip connections, leading to less than optimal results. To address these limitations, this research propounds the novel Convolution Block Dual Attention Module (CBDAM), as well as two pioneering network architectures: The Convolution Block Dual Attention Module Unet (CBDAMUNet) and the Dual Decoder Convolution Block Attention Module with Attention U-Net (D2CBDAMAttUnet). These are built upon a fortified encoder-decoder structure aimed at providing an automated, streamlined detection mechanism from fundus imagery. Thoroughly tested against the DRIVE, CHASEDB1 and STARE datasets, recognized standards in retinal vessel segmentation, the proposed models not only redefine the accuracy benchmarks but also represent a significant stride in automated retinal vessel analysis. The introduction of these advanced networks is a milestone in ophthalmologic diagnostics and research, offering a potent asset for medical professionals and specialists in the field.https://ieeexplore.ieee.org/document/10855415/Deep learningretinal vessel segmentationmedical image processingUNet networkconvolution block dual attention module |
| spellingShingle | Vo Trong Quang Huy Chih-Min Lin D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images IEEE Access Deep learning retinal vessel segmentation medical image processing UNet network convolution block dual attention module |
| title | D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images |
| title_full | D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images |
| title_fullStr | D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images |
| title_full_unstemmed | D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images |
| title_short | D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images |
| title_sort | d2cbdamattunet dual decoder convolution block dual attention unet for accurate retinal vessel segmentation from fundus images |
| topic | Deep learning retinal vessel segmentation medical image processing UNet network convolution block dual attention module |
| url | https://ieeexplore.ieee.org/document/10855415/ |
| work_keys_str_mv | AT votrongquanghuy d2cbdamattunetdualdecoderconvolutionblockdualattentionunetforaccurateretinalvesselsegmentationfromfundusimages AT chihminlin d2cbdamattunetdualdecoderconvolutionblockdualattentionunetforaccurateretinalvesselsegmentationfromfundusimages |