Purpose. We propose a neural network for fast prediction of realistic, time-parametrized deformations between pairs of input segmentations. The proposed method was used to generate a library of planning CTVs for cervical cancer radiotherapy.Methods.A 3D convolutional neural network (CNN) was introduced to predict a stationary velocity field given the distance maps of the cervix CTV in empty and full bladder anatomy. Diffeomorphic deformation trajectories between the two states were obtained by time integration. Intermediate deformation states were used to populate a library of cervix CTVs. The network was trained on cervix CTV deformations of 20 patients generated by finite element modeling (FEM). Validation was performed on FEM data of 9 healthy volunteers. Additionally, for these subjects, CTV deformations were observed in a series of repeat MR scans as the bladder filled from empty to full. Predicted and FEM libraries were compared, and benchmarked against the observed deformations. Finally, for an independent test set of 20 patients the predicted libraries were evaluated clinically, and compared to the current method.Results.The median Dice score over the validation subjects between the predicted and FEM libraries was >0.95 throughout the deformation, with a median 90 percentile surface distance of <3 mm. The ability to cover observed CTVs was similar for both the FEM-based and the proposed method, with residual offsets being about twice as large as the difference between the two methods. Clinical evaluation showed improved library properties over the method currently used in clinic.Conclusions.We proposed a CNN trained on FEM deformations, which predicts the deformation trajectory between two input states of the cervix CTV in one forward pass. We applied this to CTV library prediction for cervical cancer. The network is able to mimic FEM deformations well, while being much faster and simpler in use.
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