Automatic segmentation of organs at risk in head and neck carcinoma from radiation therapy using multi-scale fusion and attention based mechanisms
Lin Xiaowei1, Yang Ruijie2, Li Ni3, Qi Qi1
1School of Computer Science and Technology, Hainan University, Haikou 570228, China; 2Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China; 3School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
Abstract：Objective To develop a multi-scale fusion and attention mechanism based image automatic segmentation method of organs at risk (OAR) from head and neck carcinoma radiotherapy. Methods We proposed a new OAR segmentation method for medical images of heads and necks based on the U-Net convolution neural network. Spatial and channel squeeze excitation (csSE) attention block were combined with the U-Net, aiming to enhance the feature expression ability. We also proposed a multi-scale block in the U-Net encoding stage to supplement characteristic information. Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD) were used as evaluation criteria for deep learning performance. Results The segmentation of 22 OAR in the head and neck was performed according to the medical image computing computer assisted intervention (MICCAI) StructSeg2019 dataset. The proposed method improved the average segmentation accuracy by 3%-6% compared with existing methods. The average DSC in the segmentation of 22 OAR in the head and neck was 78.90% and the average 95%HD was 6.23 mm. Conclusion Automatic segmentation of OAR from the head and neck CT using multi-scale fusion and attention mechanism achieves high segmentation accuracy, which is promising for enhancing the accuracy and efficiency of radiotherapy in clinical practice.
Lin Xiaowei,Yang Ruijie,Li Ni et al. Automatic segmentation of organs at risk in head and neck carcinoma from radiation therapy using multi-scale fusion and attention based mechanisms[J]. Chinese Journal of Radiation Oncology, 2023, 32(4): 319-324.
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2023, Vol. 32 
