Discussion
deformations
CAVITY
This study demonstrated the feasibility of deep learning-based DIR to account for inter-fraction organ motion in the HDR brachytherapy. This method exploits image similarity features between fixed and moving images to train a CNN model for the DIR. The CNN model was evaluated for pelvis CT images. Since there is no specific dataset (publicly available) for such an application, a deformation simulation was introduced considering realistic image deformation within the bladder and rectum organs. In the proposed method, DIRNet architecture, introduced by de Vos et al. [To compare the proposed method to conventional intensity-based DIR algorithms, SimpleElastix was implemented on the dataset with tuned parameters. Results showed that the DIRNet 4 model that benefits from more Conv layers (eight layers, in comparison with the basic DIRNet model) achieved better results on all three organs; nevertheless, slightly increased errors were observed for the bladder due to the large deformation of this organ. It is suggested that the number of Conv layers has a significant effect on the performance of the model, in comparison with the average pooling layer. The reason is as Conv layers increase, the model can extract more discriminative features from the input images that help the transformation function to estimate better deformation parameters. On the other hand, the average pooling layer may decrease the image dimension which results in poor resolution in the deformation estimation field. However, for large image deformation within the rectum, pooling layers had a greater effect on the performance of the model, compared to the loss function. For medium deformation relative to the rectum, such as within the cervix, the loss function was important, in comparison with the pooling layer. The mean squared loss function, which magnifies the estimated errors, would show more sensitivity to small errors between the predicted and the ground-truth images. Therefore, the MSE loss function could not be a good choice for small deformations. Furthermore, for low deformation, such as bladder, most of the DIRNet models did not improve registration accuracy. However, DIRNet 1 model (the basic DIRNet model) improved bladder registration.In comparison with similar studies on the rigid-registration problem, Miao et al. [Hu et al. [In the present experiment, it was found that the main advantage of this study was conducting deformation simulation based on anatomical changes by inserting applicators inside the vaginal cavity. This simulation also improved the proposed models through the use of binary masks which resulted in pixel-level accuracy for model evaluation. Future studies will aim to further improve the proposed method by adding more Conv layers and testing different loss functions. Although a dataset of 57 cervical patients was used in this study, increasing the training dataset would result in a model with improved accuracy and generalizability by using data augmentation [