ArticleVisual computing for industry, biomedicine, and art2022
DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images.
Article in Visual computing for industry, biomedicine, and art, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review.BMC medical imaging · 2024Pooled it
- Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.Journal of neuroimaging : official journal of the American Society of NeuroimagingPooled it
- Semantic-guided 3D Gaussian splatting for sparse-view reconstruction in industrial digital twins.Visual computing for industry, biomedicine, and art · 2026Article
- Efficient medical image segmentation using RepSegNet lightweight reparameterized neural network.Scientific reports · 2026Article
- Anatomically-guided masked autoencoder pre-training for aneurysm detection.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2026Article
- Intracranial aneurysm segmentation on digital subtraction angiography: a retrospective and multi-center study.Frontiers in neurology · 2025Article
- Intracranial aneurysm segmentation with nnU-net: utilizing loss functions and automated vessel extraction.Vessel plus · 2025Article
- Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.Journal of imaging · 2023Article
- DB-DCAFN: dual-branch deformable cross-attention fusion network for bacterial segmentation.Visual computing for industry, biomedicine, and art · 2023Article
- EM-Gaze: eye context correlation and metric learning for gaze estimation.Visual computing for industry, biomedicine, and art · 2023Article
- Correction: DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images.Visual computing for industry, biomedicine, and art · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography (MRA) is essential for medical auxiliary treatments, which can effectively prevent subarachnoid hemorrhages. This paper proposes an image segmentation model based on a dense convolutional attention U-Net, which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate segmentation of MRA-acquired aneurysm images with large size differences. The U-Net model serves as a backbone, combining dense block and convolution block attention module (CBAM). The dense block is composed of a batch normalization layer, an randomly rectified linear unit activation function, and a convolutional layer, for mitigation of vanishing gradients, for multiplexing of aneurysm features, and for improving the network training efficiency. The CBAM is composed of a channel attention module and a spatial attention module, improving the segmentation performance of feature discrimination and enhancing the acquisition of key feature information. Owing to the large variation of aneurysm sizes, multi-scale fusion is performed during up-sampling, for adaptive segmentation of MRA-acquired aneurysm images. The model was tested on the MICCAI 2020 ADAM dataset, and its generalizability was validated on the clinical aneurysm dataset (aneurysm sizes: < 3 mm, 3-7 mm, and > 7 mm) supplied by the Affiliated Hospital of Qingdao University. A good clinical application segmentation performance was demonstrated.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.