HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation
Accurate segmentation of fine-grained information is an important step in medical image analysis applications. With the development of the encoder-decoder-based networks, various network structures and algorithms have made significant progress in semantic segmentation tasks. This work aims to presen...
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doaj-31566aed9cad4bbe854c3aaa0419a4822021-03-30T02:43:01ZengIEEEIEEE Access2169-35362020-01-018382103822010.1109/ACCESS.2020.29759839007452HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image SegmentationChengzhi Lyu0https://orcid.org/0000-0001-9160-0324Guoqing Hu1https://orcid.org/0000-0001-6526-2560Dan Wang2https://orcid.org/0000-0002-3272-9045School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, ChinaAccurate segmentation of fine-grained information is an important step in medical image analysis applications. With the development of the encoder-decoder-based networks, various network structures and algorithms have made significant progress in semantic segmentation tasks. This work aims to present a novel high-resolution encoder-decoder network (HRED-Net) for fine-grained image segmentation that is highly accurate for small-scale targets. We design a multiscale context connection module to extract feature information without reducing the resolution, and propose a multiresolution fusion model to fine-tune the final results. In addition, these modules are trained together with a detail-oriented loss function to enhance the model's perception of fine-grained parts. Through experiments on the DRIVE dataset, we found a balance between these modules, and our comparison results show that in addition to the extraction multiscale features, the fusion of multiresolution prediction information is also beneficial for fine-grained segmentation. Our method yielded significant improvements in the accuracy and sensitivity in retinal vessel and lung segmentation tasks.https://ieeexplore.ieee.org/document/9007452/Fine-grainedmultiscalemultiresolutionretinal vesselsemantic segmentation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chengzhi Lyu Guoqing Hu Dan Wang |
spellingShingle |
Chengzhi Lyu Guoqing Hu Dan Wang HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation IEEE Access Fine-grained multiscale multiresolution retinal vessel semantic segmentation |
author_facet |
Chengzhi Lyu Guoqing Hu Dan Wang |
author_sort |
Chengzhi Lyu |
title |
HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation |
title_short |
HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation |
title_full |
HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation |
title_fullStr |
HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation |
title_full_unstemmed |
HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation |
title_sort |
hred-net: high-resolution encoder-decoder network for fine-grained image segmentation |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Accurate segmentation of fine-grained information is an important step in medical image analysis applications. With the development of the encoder-decoder-based networks, various network structures and algorithms have made significant progress in semantic segmentation tasks. This work aims to present a novel high-resolution encoder-decoder network (HRED-Net) for fine-grained image segmentation that is highly accurate for small-scale targets. We design a multiscale context connection module to extract feature information without reducing the resolution, and propose a multiresolution fusion model to fine-tune the final results. In addition, these modules are trained together with a detail-oriented loss function to enhance the model's perception of fine-grained parts. Through experiments on the DRIVE dataset, we found a balance between these modules, and our comparison results show that in addition to the extraction multiscale features, the fusion of multiresolution prediction information is also beneficial for fine-grained segmentation. Our method yielded significant improvements in the accuracy and sensitivity in retinal vessel and lung segmentation tasks. |
topic |
Fine-grained multiscale multiresolution retinal vessel semantic segmentation |
url |
https://ieeexplore.ieee.org/document/9007452/ |
work_keys_str_mv |
AT chengzhilyu hrednethighresolutionencoderdecodernetworkforfinegrainedimagesegmentation AT guoqinghu hrednethighresolutionencoderdecodernetworkforfinegrainedimagesegmentation AT danwang hrednethighresolutionencoderdecodernetworkforfinegrainedimagesegmentation |
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1724184716588875776 |