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-Net) for liver tumor segmentation. We incorporate three novel modules in the CAFCT-Net 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 model achieves a mean Intersection over Union (IoU) of 76.54% and Dice coefficient of 84.29%, 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有助于从计算机断层扫描（CT）图像中自动识别肿瘤。本文提出了一种用于肝肿瘤分割的、融合上下文与注意力特征的卷积神经网络（CNN）与Transformer混合网络（CAFCT-Net）。我们在CAFCT-Net架构中引入了三个新颖模块：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（AGs），以增强与肿瘤边界相关的上下文信息，从而实现精确分割。实验结果表明，在肝脏肿瘤分割基准（LiTS）数据集上，所提模型的平均交并比（IoU）和Dice系数分别达到76.54%和84.29%，其性能优于纯CNN或Transformer方法，例如Attention U-Net和PVTFormer。