Author(s): Gong, YX (Gong, Yuxin); Zhu, HG (Zhu, Haogang); Li, JX (Li, Jixing); Yang, JC (Yang, Jingchun); Cheng, J (Cheng, Jian); Chang, Y (Chang, Ying); Bai, X (Bai, Xiao); Ji, XM (Ji, Xunming)

Source: COMPUTERIZED MEDICAL IMAGING AND GRAPHICS Volume: 104 Article Number: 102183 DOI: 10.1016/j.compmedimag.2023.102183 Published: MAR 2023

Abstract: The highly ambiguous nature of boundaries and similar objects is difficult to address in some ultrasound image segmentation tasks, such as neck muscle segmentation, leading to unsatisfactory performance. Thus, this paper proposes a two-stage network called SCCNet (self-correction context network) using a self-correction boundary preservation module and class-context filter to alleviate these problems. The proposed self-correction boundary preservation module uses a dynamic key boundary point (KBP) map to increase the capability of iteratively discriminating ambiguous boundary points segments, and the predicted segmentation map from one stage is used to obtain a dynamic class prior filter to improve the segmentation performance at Stage 2. Finally, three datasets, Neck Muscle, CAMUS and Thyroid, are used to demonstrate that our proposed SCCNet outperforms other state -of-the art methods, such as BPBnet, DSNnet, and RAGCnet. Our proposed network shows at least a 1.2-3.7% improvement on the three datasets, Neck Muscle, Thyroid, and CAMUS. The source code is available at https:// github.com/lijixing0425/SCCNet.

Accession Number: WOS:000926613000001

PubMed ID: 36623451

ISSN: 0895-6111

eISSN: 1879-0771

Full Text: https://www.sciencedirect.com/science/article/pii/S0895611123000010?via%3Dihub