ArticleFrontiers in neuroscience2022
Cross-Attention and Deep Supervision UNet for Lesion Segmentation of Chronic Stroke.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images.Journal of imaging informatics in medicine · 2026Article
- Quantum SVM-driven framework for accurate brain stroke classification.Scientific reports · 2026Article
- Deep Conformal Supervision: Leveraging Intermediate Features for Robust Uncertainty Quantification.Journal of imaging informatics in medicine · 2025Article
- DualPlaqueNet with dual-branch structure and attention mechanism for carotid plaque semantic segmentation and size prediction.Frontiers in physiology · 2025Article
- Segmentation of stroke lesions using transformers-augmented MRI analysis.Human brain mapping · 2024Article
- Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2024Review
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Authors and funding
4 authors.
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Abstract
Stroke is an acute cerebrovascular disease with high incidence, high mortality, and high disability rate. Determining the location and volume of the disease in MR images promotes accurate stroke diagnosis and surgical planning. Therefore, the automatic recognition and segmentation of stroke lesions has important clinical significance for large-scale stroke imaging analysis. There are some problems in the segmentation of stroke lesions, such as imbalance of the front and back scenes, uncertainty of position, and unclear boundary. To meet this challenge, this paper proposes a cross-attention and deep supervision UNet (CADS-UNet) to segment chronic stroke lesions from T1-weighted MR images. Specifically, we propose a cross-spatial attention module, which is different from the usual self-attention module. The location information interactively selects encode features and decode features to enrich the lost spatial focus. At the same time, the channel attention mechanism is used to screen the channel characteristics. Finally, combined with deep supervision and mixed loss, the model is supervised more accurately. We compared and verified the model on the authoritative open dataset "Anatomical Tracings of Lesions After Stroke" (Atlas), which fully proved the effectiveness of our model.
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