College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China.
School of Science, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China.
Phys Med Biol. 2023 Nov 6;68(22). doi: 10.1088/1361-6560/ad04a8.
Head and neck (H&N) cancers are prevalent globally, and early and accurate detection is absolutely crucial for timely and effective treatment. However, the segmentation of H&N tumors is challenging due to the similar density of the tumors and surrounding tissues in CT images. While positron emission computed tomography (PET) images provide information about the metabolic activity of the tissue and can distinguish between lesion regions and normal tissue. But they are limited by their low spatial resolution. To fully leverage the complementary information from PET and CT images, we propose a novel and innovative multi-modal tumor segmentation method specifically designed for H&N tumor segmentation.The proposed novel and innovative multi-modal tumor segmentation network (LSAM) consists of two key learning modules, namely L2-Norm self-attention and latent space feature interaction, which exploit the high sensitivity of PET images and the anatomical information of CT images. These two advanced modules contribute to a powerful 3D segmentation network based on a U-shaped structure. The well-designed segmentation method can integrate complementary features from different modalities at multiple scales, thereby improving the feature interaction between modalities.We evaluated the proposed method on the public HECKTOR PET-CT dataset, and the experimental results demonstrate that the proposed method convincingly outperforms existing H&N tumor segmentation methods in terms of key evaluation metrics, including DSC (0.8457), Jaccard (0.7756), RVD (0.0938), and HD95 (11.75).The innovative Self-Attention mechanism based on L2-Norm offers scalability and is effective in reducing the impact of outliers on the performance of the model. And the novel method for multi-scale feature interaction based on Latent Space utilizes the learning process in the encoder phase to achieve the best complementary effects among different modalities.
头颈部（H&N）癌症在全球范围内普遍存在，早期准确的检测对于及时有效的治疗至关重要。然而，由于 CT 图像中肿瘤和周围组织的密度相似，H&N 肿瘤的分割具有挑战性。虽然正电子发射断层扫描（PET）图像提供了关于组织代谢活性的信息，并且可以区分病变区域和正常组织。但它们受到其低空间分辨率的限制。为了充分利用来自 PET 和 CT 图像的互补信息，我们提出了一种专门用于 H&N 肿瘤分割的新型创新多模态肿瘤分割方法。所提出的新型创新多模态肿瘤分割网络（LSAM）由两个关键学习模块组成，即 L2-范数自注意力和潜在空间特征交互，它们利用了 PET 图像的高灵敏度和 CT 图像的解剖信息。这两个先进的模块有助于基于 U 形结构的强大 3D 分割网络。精心设计的分割方法可以在多个尺度上整合来自不同模态的互补特征，从而提高模态之间的特征交互。我们在公共 HECKTOR PET-CT 数据集上评估了所提出的方法，实验结果表明，所提出的方法在关键评估指标方面，包括 DSC（0.8457）、Jaccard（0.7756）、RVD（0.0938）和 HD95（11.75）方面，都优于现有的 H&N 肿瘤分割方法。基于 L2-范数的创新自注意力机制具有可扩展性，并且可以有效地减少异常值对模型性能的影响。基于潜在空间的新型多尺度特征交互方法利用编码器阶段的学习过程，实现了不同模态之间的最佳互补效果。
