ArticleRadiation oncology (London, England)2025
Diffusion-CSPAM U-Net: A U-Net model integrated hybrid attention mechanism and diffusion model for segmentation of computed tomography images of brain metastases.
Article in Radiation oncology (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Diffusion models for brain imaging computing: a survey of frameworks and applications.Brain informatics · 2026Review
- Are AI Neuroimaging Models Ready for Clinical Use? A Systematic Methodological Review.Journal of clinical medicine · 2026Review
- Diffusion Models in Oncology: a Review of Current Applications.Current oncology reports · 2026Review
- Magnetic resonance imaging diagnosis of knee injuries after skiing in adolescents under deep learning.Scientific reports · 2026Article
- ACLA-Net: attention-guided cross-level alignment network for MRI-based prostate cancer lesion segmentation.Frontiers in physiology · 2026Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
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Authors and funding
10 authors.
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Abstract
backgroundBrain metastases are common complications in patients with cancer and significantly affect prognosis and treatment strategies. The accurate segmentation of brain metastases is crucial for effective radiation therapy planning. However, in resource-limited areas, the unavailability of MRI imaging is a significant challenge that necessitates the development of reliable segmentation models for computed tomography images (CT). PURPOSE: This study aimed to develop and evaluate a Diffusion-CSPAM-U-Net model for the segmentation of brain metastases on CT images and thereby provide a robust tool for radiation oncologists in regions where magnetic resonance imaging (MRI) is not accessible.
methodsThe proposed Diffusion-CSPAM-U-Net model integrates diffusion models with channel-spatial-positional attention mechanisms to enhance the segmentation performance. The model was trained and validated on a dataset consisting of CT images from two centers (n = 205) and (n = 45). Performance metrics, including the Dice similarity coefficient (DSC), intersection over union (IoU), accuracy, sensitivity, and specificity, were calculated. Additionally, this study compared models proposed for brain metastases of different sizes with those proposed in other studies.
resultsThe diffusion-CSPAM-U-Net model achieved promising results on the external validation set. Overall average DSC of 79.3% ± 13.3%, IoU of 69.2% ± 13.3%, accuracy of 95.5% ± 11.8%, sensitivity of 80.3% ± 12.1%, specificity of 93.8% ± 14.0%, and HD of 5.606 ± 0.990 mm were measured. These results demonstrate favorable improvements over existing models.
conclusionsThe diffusion-CSPAM-U-Net model showed promising results in segmenting brain metastases in CT images, particularly in terms of sensitivity and accuracy. The proposed diffusion-CSPAM-U-Net model provides an effective tool for radiation oncologists for the segmentation of brain metastases in CT images.
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