论文标题
多模式PET-CT分段的超连接变压器网络
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation
论文作者
论文摘要
[18F] -FluorDeoxyglucose（FDG）正电子发射断层扫描 - 计算机断层扫描（PET-CT）已成为诊断许多癌症的首选成像方式。共同学习互补的PET-CT成像特征是自动肿瘤分割和开发计算机辅助癌症诊断系统的基本要求。在这项研究中，我们提出了一个超连接的变压器（HCT）网络，该网络将变压器网络（TN）与多模式PET-CT图像的超连接融合整合在一起。 TN因其在图像特征学习中提供全局依赖性的能力而受到利用，这是通过使用具有自我发挥机制的图像贴片嵌入来捕获整个图像范围的上下文信息来实现的。我们使用基于多个TN的分支扩展了TN的单模式定义，以分别提取图像特征。我们还引入了超连接的融合，以迭代方式融合了多个变压器之间的上下文和互补图像特征。我们使用两个临床数据集的结果表明，与现有方法相比，HCT在分割准确性方面取得了更好的性能。
[18F]-Fluorodeoxyglucose (FDG) positron emission tomography - computed tomography (PET-CT) has become the imaging modality of choice for diagnosing many cancers. Co-learning complementary PET-CT imaging features is a fundamental requirement for automatic tumor segmentation and for developing computer aided cancer diagnosis systems. In this study, we propose a hyper-connected transformer (HCT) network that integrates a transformer network (TN) with a hyper connected fusion for multi-modality PET-CT images. The TN was leveraged for its ability to provide global dependencies in image feature learning, which was achieved by using image patch embeddings with a self-attention mechanism to capture image-wide contextual information. We extended the single-modality definition of TN with multiple TN based branches to separately extract image features. We also introduced a hyper connected fusion to fuse the contextual and complementary image features across multiple transformers in an iterative manner. Our results with two clinical datasets show that HCT achieved better performance in segmentation accuracy when compared to the existing methods.
