Edge-guided multi-scale adaptive feature fusion network for liver tumor segmentation

• Published in: Scientific reports Vol. 14; no. 1; pp. 28370 - 14
• Main Authors: Zhang, Tiange, Liu, Yuefeng, Zhao, Qiyan, Xue, Guoyue, Shen, Hongyu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17-11-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1564; 639/166/985; Adaptive feature fusion; Boundaries; Computed tomography; Diagnosis; Edge sensing guided; Humanities and Social Sciences; Image processing; Liver; Liver cancer; Liver tumor segmentation; Medical image; multidisciplinary; Science; Science (multidisciplinary); Segmentation; Tumors; Medical image; Liver tumor segmentation; Edge sensing guided; Adaptive feature fusion
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-79379-y

Automated segmentation of liver tumors on CT scans is essential for aiding diagnosis and assessing treatment. Computer-aided diagnosis can reduce the costs and errors associated with manual processes and ensure the provision of accurate and reliable clinical assessments. However, liver tumors in CT images vary significantly in size and have fuzzy boundaries, making it difficult for existing methods to achieve accurate segmentation. Therefore, this paper proposes MAEG-Net, a multi-scale adaptive feature fusion liver tumor segmentation network based on edge guidance. Specifically, we design a multi-scale adaptive feature fusion module that effectively incorporates multi-scale information to better guide the segmentation of tumors of different sizes. Additionally, to address the problem of blurred tumor boundaries in images, we introduce an edge-aware guidance module to improve the model's feature learning ability under these conditions. Evaluation results on the liver tumor dataset (LiTS2017) show that our method achieves a Dice coefficient of 71.84% and a VOE of 38.64%, demonstrating the best performance for liver tumor segmentation in CT images.