U2-Attention-Net: a deep learning automatic delineation model for parotid glands in head and neck cancer organs at risk on radiotherapy localization computed tomography images

Abstract

Objective

This study aimed to develop a novel deep learning model, U₂-Attention-Net (U₂A-Net), for precise segmentation of parotid glands on radiotherapy localization CT images.

Methods

CT images from 79 patients with head and neck cancer were selected, on which the label maps were delineated by relevant practitioners to construct a dataset. The dataset was divided into the training set (n = 60), validation set (n = 6), and test set (n = 13), with the training set augmented. U₂A-Net, divided into U₂A-Net V₁ (sSE) and U₂A-Net V₂ (cSE) based on different attention mechanisms, was evaluated for parotid gland segmentation based on the DL loss function with U-Net, Attention U-Net, DeepLabV3+, and TransUNet as comparision models. Segmentation was also performed using GDL and GD-BCEL loss functions. Model performance was evaluated using DSC, JSC, PPV, SE, HD, RVD, and VOE metrics.

Results

The quantitative results revealed that U₂A-Net based on DL outperformed the comparative models. While U₂A-Net V₁ had the highest PPV, U₂A-Net V₂ demonstrated the best quantitative results in other metrics. Qualitative results showed that U₂A-Net’s segmentation closely matched expert delineations, reducing oversegmentation and undersegmentation, with U₂A-Net V₂ being more effective. In comparing loss functions, U₂A-Net V₁ using GD-BCEL and U₂A-Net V₂ using DL performed best.

Conclusion

The U₂A-Net model significantly improved parotid gland segmentation on radiotherapy localization CT images. The cSE attention mechanism showed advantages with DL, while sSE performed better with GD-BCEL.