A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation
Morphological and functional changes in retinal vessels are indicators of a variety of chronic diseases, such as diabetes, stroke, and hypertension. However, without a large number of high-quality annotations, existing deep learning-based medical image segmentation approaches may degrade their perfo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/21/3538 |
| _version_ | 1797468508665151488 |
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| author | Zhonghao Tu Qian Zhou Hua Zou Xuedong Zhang |
| author_facet | Zhonghao Tu Qian Zhou Hua Zou Xuedong Zhang |
| author_sort | Zhonghao Tu |
| collection | DOAJ |
| description | Morphological and functional changes in retinal vessels are indicators of a variety of chronic diseases, such as diabetes, stroke, and hypertension. However, without a large number of high-quality annotations, existing deep learning-based medical image segmentation approaches may degrade their performance dramatically on the retinal vessel segmentation task. To reduce the demand of high-quality annotations and make full use of massive unlabeled data, we propose a self-supervised multi-task strategy to extract curvilinear vessel features for the retinal vessel segmentation task. Specifically, we use a dense network to extract more vessel features across different layers/slices, which is elaborately designed for hardware to train and test efficiently. Then, we combine three general pre-training tasks (i.e., intensity transformation, random pixel filling, in-painting and out-painting) in an aggregated way to learn rich hierarchical representations of curvilinear retinal vessel structures. Furthermore, a vector classification task module is introduced as another pre-training task to obtain more spatial features. Finally, to make the segmentation network pay more attention to curvilinear structures, a novel dynamic loss is proposed to learn robust vessel details from unlabeled fundus images. These four pre-training tasks greatly reduce the reliance on labeled data. Moreover, our network can learn the retinal vessel features effectively in the pre-training process, which leads to better performance in the target multi-modal segmentation task. Experimental results show that our method provides a promising direction for the retinal vessel segmentation task. Compared with other state-of-the-art supervised deep learning-based methods applied, our method requires less labeled data and achieves comparable segmentation accuracy. For instance, we match the accuracy of the traditional supervised learning methods on DRIVE and Vampire datasets without needing any labeled ground truth image. With elaborately training, we gain the 0.96 accuracy on DRIVE dataset. |
| first_indexed | 2024-03-09T19:08:25Z |
| format | Article |
| id | doaj.art-77803f6ba7d34a2b943ae6dce9549a5b |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T19:08:25Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-77803f6ba7d34a2b943ae6dce9549a5b2023-11-24T04:25:31ZengMDPI AGElectronics2079-92922022-10-011121353810.3390/electronics11213538A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel SegmentationZhonghao Tu0Qian Zhou1Hua Zou2Xuedong Zhang3School of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Information Engineering, Tarim University, Alaer 843300, ChinaMorphological and functional changes in retinal vessels are indicators of a variety of chronic diseases, such as diabetes, stroke, and hypertension. However, without a large number of high-quality annotations, existing deep learning-based medical image segmentation approaches may degrade their performance dramatically on the retinal vessel segmentation task. To reduce the demand of high-quality annotations and make full use of massive unlabeled data, we propose a self-supervised multi-task strategy to extract curvilinear vessel features for the retinal vessel segmentation task. Specifically, we use a dense network to extract more vessel features across different layers/slices, which is elaborately designed for hardware to train and test efficiently. Then, we combine three general pre-training tasks (i.e., intensity transformation, random pixel filling, in-painting and out-painting) in an aggregated way to learn rich hierarchical representations of curvilinear retinal vessel structures. Furthermore, a vector classification task module is introduced as another pre-training task to obtain more spatial features. Finally, to make the segmentation network pay more attention to curvilinear structures, a novel dynamic loss is proposed to learn robust vessel details from unlabeled fundus images. These four pre-training tasks greatly reduce the reliance on labeled data. Moreover, our network can learn the retinal vessel features effectively in the pre-training process, which leads to better performance in the target multi-modal segmentation task. Experimental results show that our method provides a promising direction for the retinal vessel segmentation task. Compared with other state-of-the-art supervised deep learning-based methods applied, our method requires less labeled data and achieves comparable segmentation accuracy. For instance, we match the accuracy of the traditional supervised learning methods on DRIVE and Vampire datasets without needing any labeled ground truth image. With elaborately training, we gain the 0.96 accuracy on DRIVE dataset.https://www.mdpi.com/2079-9292/11/21/3538self-supervised learningvessel segmentationmulti-task networkaggregation task |
| spellingShingle | Zhonghao Tu Qian Zhou Hua Zou Xuedong Zhang A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation Electronics self-supervised learning vessel segmentation multi-task network aggregation task |
| title | A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation |
| title_full | A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation |
| title_fullStr | A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation |
| title_full_unstemmed | A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation |
| title_short | A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation |
| title_sort | multi task dense network with self supervised learning for retinal vessel segmentation |
| topic | self-supervised learning vessel segmentation multi-task network aggregation task |
| url | https://www.mdpi.com/2079-9292/11/21/3538 |
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