ArticleBMC medical imaging2023
Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture.
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A cloud-edge adaptive lightweight network with dynamic inference enables real-time ultrasound image segmentation.Scientific reports · 2026Article
- A novel diffuse liver nodule detector via integrating semantic edge features and probabilistic uncertainty modeling.Frontiers in artificial intelligence · 2026Article
- O-CCR: oriented cervical canal region detection framework toward cervical change assessment in transvaginal ultrasound.BMC medical imaging · 2025Article
- A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation.Scientific reports · 2025Article
- A concept for fully automated segmentation of bone in ultrasound imaging.Scientific reports · 2025Article
- Enhancing Lesion Segmentation in Ultrasound Images: The Impact of Targeted Data Augmentation Strategies.International journal of biomedical imaging · 2025Article
- Exploratory study on the enhancement of O-RADS application effectiveness for novice ultrasonographers via deep learning.Archives of gynecology and obstetrics · 2024Article
- Three-dimensional visualization of thyroid ultrasound images based on multi-scale features fusion and hierarchical attention.Biomedical engineering online · 2024Article
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8 authors.
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Abstract
backgroundIdentifying thyroid nodules' boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and glands.
methodsThe 5822 ultrasound images used in the experiment came from two centers, 4658 images were used as the training dataset, and 1164 images were used as the independent mixed test dataset finally. Based on U-Net, deformable-pyramid split-attention residual U-Net (DSRU-Net) by introducing ResNeSt block, atrous spatial pyramid pooling, and deformable convolution v3 was proposed. This method combined context information and extracts features of interest better, and had advantages in segmenting nodules and glands of different shapes and sizes.
resultsDSRU-Net obtained 85.8% mean Intersection over Union, 92.5% mean dice coefficient and 94.1% nodule dice coefficient, which were increased by 1.8%, 1.3% and 1.9% compared with U-Net.
conclusionsOur method is more capable of identifying and segmenting glands and nodules than the original method, as shown by the results of correlational studies.
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