An ultrasound image segmentation method for thyroid nodules based on dual-path attention mechanism-enhanced UNet++
The segmentation model proposed in this study can effectively capture global contextual information in ultrasound images and accurately identify the locations of nodule areas. The model yields excellent segmentation results, especially for small and multiple nodules. Additionally, the integrated loss function improves the segmentation of nodule edges, enhancing the model's accuracy in segmenting edge details.
Authors
- Ronghua Zhang (ORCID: https://orcid.org/0000-0001-6004-6471)
- Jiale Hu (ORCID: https://orcid.org/0009-0001-1664-2935)
- Xiang Li (ORCID: https://orcid.org/0000-0002-0761-0931)
- Yongqiang Yang
- Jun Li (ORCID: https://orcid.org/0000-0003-0124-1757)
- Wenyu Liu (ORCID: https://orcid.org/0000-0002-4582-7488)
- Changzheng Liu
- Peizhen Dong
Institutions
- Shihezi University (CN)
- First Affiliated Hospital of Shihezi University Medical College (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12880-024-01521-z
- Citations
- 12
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.85
Funders
- Shihezi University
- Xinjiang Production and Construction Corps