Academic Journal
FAU-Net: Fixup Initialization Channel Attention Neural Network for Complex Blood Vessel Segmentation
| Title: | FAU-Net: Fixup Initialization Channel Attention Neural Network for Complex Blood Vessel Segmentation |
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| Authors: | Dongjin Huang, Liwen Yin, Hao Guo, Wen Tang, Tao Ruan Wan |
| Source: | Applied Sciences, Vol 10, Iss 18, p 6280 (2020) |
| Publisher Information: | MDPI AG, 2020. |
| Publication Year: | 2020 |
| Collection: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Subject Terms: | medical image segmentation, de-normalization, channel attention mechanism, u-net, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| More Details: | Medical image segmentation based on deep learning is a central research issue in the field of computer vision. Many existing segmentation networks can achieve accurate segmentation using fewer data sets. However, they have disadvantages such as poor network flexibility and do not adequately consider the interdependence between feature channels. In response to these problems, this paper proposes a new de-normalized channel attention network, which uses an improved de-normalized residual block structure and a new channel attention module in the network for the segmentation of sophisticated vessels. The de-normalized network sends the extracted rough features to the channel attention network. The channel attention module can explicitly model the interdependence between channels and pay attention to the correlation with crucial information in multiple feature channels. It can focus on the channels with the most association with vital information among multiple feature channels, and get more detailed feature results. Experimental results show that the network proposed in this paper is feasible, is robust, can accurately segment blood vessels, and is particularly suitable for complex blood vessel structures. Finally, we compared and verified the network proposed in this paper with the state-of-the-art network and obtained better experimental results. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/10/18/6280; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app10186280 |
| Access URL: | https://doaj.org/article/108dca6e852d4374849023e1122fbf9d |
| Accession Number: | edsdoj.108dca6e852d4374849023e1122fbf9d |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20763417 |
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| DOI: | 10.3390/app10186280 |
| Published in: | Applied Sciences |
| Language: | English |