Improved U-Net network model based on knowledge distillation for segmenting oral and maxillofacial tumor on CT images
Objective To observe the value of an improved U-Net network model based on knowledge distillation for segmenting oral and maxillofacial tumors on CT images.Methods Totally 609 CT images of 121 patients with oral and maxillofacial tumors from 2 medical centers were collected.Meanwhile,1 977 CT images of 254 patients with oral and maxillofacial tumors in public dataset HECKTOR2020 were selected.The multi-scale and attention mechanisms were introduced into U-Net network model to establish an improved U-Net model combining with residual network.Knowledge distillation technology was used to generate student models.The efficacy of the improved U-Net model for segmenting oral and maxillofacial tumor on CT images was observed.Results The improved U-Net model had a size of 89.30 MB,a parameter count of 17.82 M and a computational load of 22.13 GFlops.The Precision,Recall,Dice similarity coefficient and intersection over union of the improved U-Net for segmenting oral and maxillofacial tumors on CT images was 0.835,0.787,0.812,and 0.761,respectively,superior to those of models established with previous methods combined with conventional Dice Loss function and unimproved model.Except for Precision,the model had relatively small difference with its teacher model.Conclusion Improved U-Net network model based on knowledge distillation was valuable for segmenting oral and maxillofacial tumors on CT images.
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