ArticleBiomedical engineering online2024
Three-dimensional visualization of thyroid ultrasound images based on multi-scale features fusion and hierarchical attention.
Article in Biomedical engineering online, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Automated segmentation of thyroid tissue and carotid artery in ultrasound videos using expert-in-the-loop deep learning: a foundational step toward AI-assisted thyroid diagnostics.Updates in surgery · 2026Article
- A cloud-edge adaptive lightweight network with dynamic inference enables real-time ultrasound image segmentation.Scientific reports · 2026Article
- Quantitative Ultrasound Grayscale Analysis and Size of Benign and Malignant Solid Thyroid Nodules.Tomography (Ann Arbor, Mich.) · 2025Article
- A narrative review on innovations of thyroid nodule ultrasound diagnosis: applications of robot and artificial intelligence technology.Gland surgery · 2025Review
- A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation.Scientific reports · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundUltrasound three-dimensional visualization, a cutting-edge technology in medical imaging, enhances diagnostic accuracy by providing a more comprehensive and readable portrayal of anatomical structures compared to traditional two-dimensional ultrasound. Crucial to this visualization is the segmentation of multiple targets. However, challenges like noise interference, inaccurate boundaries, and difficulties in segmenting small structures exist in the multi-target segmentation of ultrasound images. This study, using neck ultrasound images, concentrates on researching multi-target segmentation methods for the thyroid and surrounding tissues.
methodWe improved the Unet++ to propose PA-Unet++ to enhance the multi-target segmentation accuracy of the thyroid and its surrounding tissues by addressing ultrasound noise interference. This involves integrating multi-scale feature information using a pyramid pooling module to facilitate segmentation of structures of various sizes. Additionally, an attention gate mechanism is applied to each decoding layer to progressively highlight target tissues and suppress the impact of background pixels.
resultsVideo data obtained from 2D ultrasound thyroid serial scans served as the dataset for this paper.4600 images containing 23,000 annotated regions were divided into training and test sets at a ratio of 9:1, the results showed that: compared with the results of U-net++, the Dice of our model increased from 78.78% to 81.88% (+ 3.10%), the mIOU increased from 73.44% to 80.35% (+ 6.91%), and the PA index increased from 92.95% to 94.79% (+ 1.84%).
conclusionsAccurate segmentation is fundamental for various clinical applications, including disease diagnosis, treatment planning, and monitoring. This study will have a positive impact on the improvement of 3D visualization capabilities and clinical decision-making and research in the context of ultrasound image.
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