Medical image semantic segmentation techniques can help identify tumors automatically from computed tomography (CT) scans. In this paper, we propose a Contextual and Attentional feature Fusions enhanced Convolutional Neural Network (CNN) and Transformer hybrid network (CAFCT) model for liver tumor segmentation. In the proposed model, three other modules are introduced in the network architecture: Attentional Feature Fusion (AFF), Atrous Spatial Pyramid Pooling (ASPP) of DeepLabv3, and Attention Gates (AGs) to improve contextual information related to tumor boundaries for accurate segmentation. Experimental results show that the proposed CAFCT achieves a mean Intersection over Union (IoU) of 90.38% and Dice score of 86.78%, respectively, on the Liver Tumor Segmentation Benchmark (LiTS) dataset, outperforming pure CNN or Transformer methods, e.g., Attention U-Net, and PVTFormer.

翻译：医学图像语义分割技术可辅助从计算机断层扫描（CT）中自动识别肿瘤。本文提出一种结合上下文与注意力特征融合的卷积神经网络（CNN）与Transformer混合网络（CAFCT）模型，用于肝脏肿瘤分割。该模型在网络架构中引入三个额外模块：注意力特征融合（AFF）、DeepLabv3的空洞空间金字塔池化（ASPP）以及注意力门控（AGs），以增强与肿瘤边界相关的上下文信息，实现精准分割。实验结果表明，所提出的CAFCT在肝脏肿瘤分割基准（LiTS）数据集上，平均交并比（IoU）达90.38%，Dice评分达86.78%，性能优于纯CNN或Transformer方法（如Attention U-Net、PVTFormer）。