Biomedical engineering online · 2025
Attention-enhanced residual U-Net: lymph node segmentation method with bimodal MRI images.
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Qiu Juping, Chen Chunxia, Li Ming, Hong Jingde, Dong Binhua, Xu Shangyue, Lin Yongping
Аннотация
OBJECTIVES: In medical images, lymph nodes (LNs) have fuzzy boundaries, diverse shapes and sizes, and structures similar to surrounding tissues. To automatically segment uterine LNs from sagittal magnetic resonance (MRI) scans, we combined T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) images and tested the final results in our proposed model.
METHODS: This study used a data set of 158 MRI images of patients with FIGO staged LN confirmed by pathology. To improve the robustness of the model, data augmentation was applied to expand the data set. The training data was manually annotated by two experienced radiologists. The DWI and T2 images were fused and inputted into U-Net. The efficient channel attention (ECA) module was added to U-Net. A residual network was added to the encoding-decoding stage, named Efficient residual U-Net (ERU-Net), to obtain the final segmentation results and calculate the mean intersection-over-union (mIoU).
RESULTS: The experimental results demonstrated that the ERU-Net network showed strong segmentation performance, which was significantly better than other segmentation networks. The mIoU reached 0.83, and the average pixel accuracy was 0.91. In addition, the precision was 0.90, and the corresponding recall was 0.91.
CONCLUSIONS: In this study, ERU-Net successfully achieved the segmentation of LN in uterine MRI images. Compared with other segmentation networks, our network has the best segmentation effect on uterine LN. This provides a valuable reference for doctors to develop more effective and efficient treatment plans.
METHODS: This study used a data set of 158 MRI images of patients with FIGO staged LN confirmed by pathology. To improve the robustness of the model, data augmentation was applied to expand the data set. The training data was manually annotated by two experienced radiologists. The DWI and T2 images were fused and inputted into U-Net. The efficient channel attention (ECA) module was added to U-Net. A residual network was added to the encoding-decoding stage, named Efficient residual U-Net (ERU-Net), to obtain the final segmentation results and calculate the mean intersection-over-union (mIoU).
RESULTS: The experimental results demonstrated that the ERU-Net network showed strong segmentation performance, which was significantly better than other segmentation networks. The mIoU reached 0.83, and the average pixel accuracy was 0.91. In addition, the precision was 0.90, and the corresponding recall was 0.91.
CONCLUSIONS: In this study, ERU-Net successfully achieved the segmentation of LN in uterine MRI images. Compared with other segmentation networks, our network has the best segmentation effect on uterine LN. This provides a valuable reference for doctors to develop more effective and efficient treatment plans.