计算机工程与应用2024，Vol.60Issue(4)：270-279,10.DOI:10.3778/j.issn.1002-8331.2210-0084
融合卷积和Transformer的多尺度肝肿瘤分割方法
Multi-Scale Liver Tumor Segmentation Algorithm by Fusing Convolution and Transformer
陈丽芳 1罗世勇1
作者信息
- 1. 江南大学 人工智能与计算机学院,江苏 无锡 214122
- 折叠
摘要
Abstract
Accurate automatic segmentation methods for liver and liver tumors are important in helping physicians to diagnose,treat,and observe liver cancer in the postoperative period.Due to the intrinsic locality of convolution,existing convolution-based methods are difficult to establish long-range dependencies.Transformer's cascading attention mecha-nism can establish global information association but will destroy local details.Based on this,a feature modeling method that fuses convolution and Transformer is proposed.The method interactively fuses local and global representations by mixed embedding to maximize the global dependencies at different resolutions.Meanwhile,the contextual information from different encoding stages is captured by multi-level feature fusion module at the skip connection to obtain richer se-mantic information.Finally,in order to cope with the variation of liver tumors in size and shape,a deformable multi-scale module is used to extract multi-scale features of tumors.The experiments mainly use Dice similarity coefficient(DSC)as evaluation metrics.The DSCs of liver and tumor on the LiTS17 dataset are 0.920 and 0.748,respectively,and the results show that the proposed network has more accurate liver tumor segmentation results compared to the baseline.关键词
医学图像/肿瘤分割/Transformer/卷积神经网络/多尺度/特征融合Key words
medical image/tumor segmentation/Transformer/convolutional neural network/multi-scale/feature fusion分类
信息技术与安全科学引用本文复制引用
陈丽芳,罗世勇..融合卷积和Transformer的多尺度肝肿瘤分割方法[J].计算机工程与应用,2024,60(4):270-279,10.
