EFU Net: Edge Information Fused 3D Unet for Brain Tumor Segmentation
| dc.contributor.author | Wang, Y. | |
| dc.contributor.author | Tian, H. | |
| dc.contributor.author | Liu, M. | |
| dc.coverage.issue | 3 | cs |
| dc.coverage.volume | 33 | cs |
| dc.date.accessioned | 2025-04-04T11:12:55Z | |
| dc.date.available | 2025-04-04T11:12:55Z | |
| dc.date.issued | 2024-09 | cs |
| dc.description.abstract | Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly segment brain tumors using computers because of the mutual infiltration and fuzzy boundary between the focus areas and the normal brain tissue. To address the above issues, a segmentation method which integrates edge features is proposed in this paper. The overall segmentation architecture follows the encoder decoder structure, extracting rich features from the encoder. The first two layers of features are input to the edge attention module, and to extract tumor edge features which are fully fused with the features of the decoder segment. At the same time, an adaptive weighted mixed loss function is introduced to train the network by adaptively adjusting the weights of different loss parts in the training process. Relevant experiments were carried out using the public brain tumor data set. The Dice mean values of the proposed segmentation model in the whole tumor area (WT), the core tumor area (TC), and the enhancing tumor area (ET) reach 91.10%, 87.16%, and 88.86%, respectively, and the mean values of Hausdorff distance are 3.92, 5.12, and 1.92 mm, respectively. The experimental results showed that the proposed method can significantly improve segmentation accuracy, especially the segmentation effect of the edge part. | en |
| dc.format | text | cs |
| dc.format.extent | 387-396 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Radioengineering. 2024 vol. 33, iss. 3, s. 387-396. ISSN 1210-2512 | cs |
| dc.identifier.doi | 10.13164/re.2024.0387 | en |
| dc.identifier.issn | 1210-2512 | |
| dc.identifier.uri | https://hdl.handle.net/11012/250713 | |
| dc.language.iso | en | cs |
| dc.publisher | Radioengineering Society | cs |
| dc.relation.ispartof | Radioengineering | cs |
| dc.relation.uri | https://www.radioeng.cz/fulltexts/2024/24_03_0387_0396.pdf | cs |
| dc.rights | Creative Commons Attribution 4.0 International license | en |
| dc.rights.access | openAccess | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Deep learning | en |
| dc.subject | brain tumor segmentation | en |
| dc.subject | encoder decoder structure | en |
| dc.subject | edge attention mechanism | en |
| dc.subject | hybrid loss function | en |
| dc.title | EFU Net: Edge Information Fused 3D Unet for Brain Tumor Segmentation | en |
| dc.type.driver | article | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.faculty | Fakulta elektrotechniky a komunikačních technologií | cs |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- 24_03_0387_0396.pdf
- Size:
- 1.5 MB
- Format:
- Adobe Portable Document Format
- Description: