DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation
| Title: | DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation |
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| Authors: | Haiyan Liu, Yu Zeng, Hao Li, Fuxin Wang, Jianjun Chang, Huaping Guo, Jian Zhang |
| Source: | IET Systems Biology, Vol 18, Iss 6, Pp 285-297 (2024) |
| Publisher Information: | Wiley, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Biology (General) |
| Subject Terms: | bioinformatics, image segmentation, learning (artificial intelligence), medical image processing, patient diagnosis, Biology (General), QH301-705.5 |
| More Details: | Abstract Intracranial haemorrhage (ICH) is an urgent and potentially fatal medical condition caused by brain blood vessel rupture, leading to blood accumulation in the brain tissue. Due to the pressure and damage it causes to brain tissue, ICH results in severe neurological impairment or even death. Recently, deep neural networks have been widely applied to enhance the speed and precision of ICH detection yet they are still challenged by small or subtle hemorrhages. The authors introduce DDANet, a novel haematoma segmentation model for brain CT images. Specifically, a dilated convolution pooling block is introduced in the intermediate layers of the encoder to enhance feature extraction capabilities of middle layers. Additionally, the authors incorporate a self‐attention mechanism to capture global semantic information of high‐level features to guide the extraction and processing of low‐level features, thereby enhancing the model's understanding of the overall structure while maintaining details. DDANet also integrates residual networks, channel attention, and spatial attention mechanisms for joint optimisation, effectively mitigating the severe class imbalance problem and aiding the training process. Experiments show that DDANet outperforms current methods, achieving the Dice coefficient, Jaccard index, sensitivity, accuracy, and a specificity of 0.712, 0.601, 0.73, 0.997, and 0.998, respectively. The code is available at https://github.com/hpguo1982/DDANet. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1751-8857 1751-8849 |
| Relation: | https://doaj.org/toc/1751-8849; https://doaj.org/toc/1751-8857 |
| DOI: | 10.1049/syb2.12103 |
| Access URL: | https://doaj.org/article/79c1face421c4f29aefa7a9cddce8b6c |
| Accession Number: | edsdoj.79c1face421c4f29aefa7a9cddce8b6c |
| Database: | Directory of Open Access Journals |
| ISSN: | 17518857 17518849 |
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| DOI: | 10.1049/syb2.12103 |
| Published in: | IET Systems Biology |
| Language: | English |