School of Automation, Central South University, Changsha Hunan 410083, China.
Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China.
Curr Med Imaging. 2023;19(10):1114-1123. doi: 10.2174/1573405619666221014114953.
Liver and tumor segmentation from CT images is a complex and crucial step in achieving full-course adaptive radiotherapy and also plays an essential role in computer-aided clinical diagnosis systems. Deep learning-based methods play an important role in achieving automatic segmentation.
This research aims to improve liver tumor detection performance by proposing a dual path feature extracting strategy and employing Swin-Transformer.
The hierarchical Swin-Transformer is embedded into the encoder and decoder and combined with CNN to form a dual coding path structure incorporating long-range dependencies and multi-scale contextual connections to capture coarse-tuned features at different semantic scales. The features of the two encoding paths and the upsampling path are fused, tested and validated with LITS and in-house datasets.
The proposed method has a DG of 97.95% and a DC of 96.2% for liver segmentation; a DG of 80.6% and a DC of 68.1% for tumor segmentation; and a classification study of the tumor dataset shows a DG of 91.1% and a DC of 87.2% for large and continuous tumors and a DG of 71.7% and a DC of 66.4% for small and scattered tumors.
Swin-Transformer can be used as a robust encoder for medical image segmentation networks and, combined with CNN networks, can better recover local spatial information and enhance feature representation. Accurate localization before segmentation can achieve better results for small and scattered tumors.
从 CT 图像中进行肝脏和肿瘤分割是实现全程自适应放疗的复杂而关键的步骤，也是计算机辅助临床诊断系统的重要组成部分。基于深度学习的方法在实现自动分割方面发挥着重要作用。
本研究旨在通过提出双路径特征提取策略并采用 Swin-Transformer 来提高肝脏肿瘤检测性能。
层次化的 Swin-Transformer 被嵌入到编码器和解码器中，并与 CNN 结合形成一个双编码路径结构，结合了长程依赖关系和多尺度上下文连接，以在不同语义尺度上捕获粗调特征。两个编码路径和上采样路径的特征被融合，在 LITS 和内部数据集上进行测试和验证。
所提出的方法在肝脏分割方面的 DG 为 97.95%，DC 为 96.2%；在肿瘤分割方面的 DG 为 80.6%，DC 为 68.1%；对肿瘤数据集的分类研究表明，大而连续肿瘤的 DG 为 91.1%，DC 为 87.2%，小而分散肿瘤的 DG 为 71.7%，DC 为 66.4%。
Swin-Transformer 可作为医学图像分割网络的强大编码器，与 CNN 网络结合，可以更好地恢复局部空间信息并增强特征表示。在分割前进行准确的定位可以实现对小而分散肿瘤的更好结果。
