MRLA-Net: A tumor segmentation network embedded with a multiple receptive-field lesion attention module in PET-CT images

• Published in: Computers in biology and medicine 2023-02, Vol.153, p.106538-106538, Article 106538
• Main Authors: Zhou, Yang, Jiang, Huiyan, Diao, Zhaoshuo, Tong, Guoyu, Luan, Qiu, Li, Yaming, Li, Xuena
• Format: Article
• Language: English
• Subjects: Attention module; Computed tomography; Datasets; Frequency dependence; Humans; Image processing; Image Processing, Computer-Assisted; Image segmentation; Lesions; Liver; Liver cancer; Liver Neoplasms; Localization; Medical imaging; Modules; Multi-modal learning; PET-CT; Positron emission; Positron Emission Tomography Computed Tomography; Sarcoma; Soft tissue sarcoma; Soft tissues; Tumor segmentation; Tumors; Visual field; Visual fields
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.106538

The tumor image segmentation is an important basis for doctors to diagnose and formulate treatment planning. PET-CT is an extremely important technology for recognizing the systemic situation of diseases due to the complementary advantages of their respective modal information. However, current PET-CT tumor segmentation methods generally focus on the fusion of PET and CT features. The fusion of features will weaken the characteristics of the modality itself. Therefore, enhancing the modal features of the lesions can obtain optimized feature sets, which is extremely necessary to improve the segmentation results. This paper proposed an attention module that integrates the PET-CT diagnostic visual field and the modality characteristics of the lesion, that is, the multiple receptive-field lesion attention module. This paper made full use of the spatial domain, frequency domain, and channel attention, and proposed a large receptive-field lesion localization module and a small receptive-field lesion enhancement module, which together constitute the multiple receptive-field lesion attention module. In addition, a network embedded with a multiple receptive-field lesion attention module has been proposed for tumor segmentation. This paper conducted experiments on a private liver tumor dataset as well as two publicly available datasets, the soft tissue sarcoma dataset, and the head and neck tumor segmentation dataset. The experimental results showed that the proposed method achieves excellent performance on multiple datasets, and has a significant improvement compared with DenseUNet, and the tumor segmentation results on the above three PET/CT datasets were improved by 7.25%, 6.5%, 5.29% in Dice per case. Compared with the latest PET-CT liver tumor segmentation research, the proposed method improves by 8.32%. •Proposed a new attention mechanism for tumor segmentation in PET-CT.•A plug and play attention module and a new tumor segmentation network are proposed.•Proposed attention mechanism from the perspective of PET-CT modal and lesion.