VoxelMorph++: a convolutional neural network architecture for unsupervised CBCT to CT deformable image registration

Abstract

We use an unsupervised method based on the VoxelMorph architecture for Cone-beam computed tomography (CBCT) to CT deformable image registration (DIR), and propose VoxelMorph++, a new architecture for predicting the deformation vector field (DVF). The proposed architecture (1) overcomes the limitation that the optimal depth of encoder-decoder is unknown, by forming a nested structure where each feature with varying depth in the encoder path has a corresponding depth decoder; (2) fuses features of varying semantic scales more flexibly by redesigning skip connections. In the testing phase, we used ITK-SNAP software to semi-automatically segment the patients’ lung regions as labels to solve the problem of expensive manual labelling. We evaluated these two architectures using lung region registration results from 10 patients’ CBCT and CT images. After registration, the mean Dice score improved from 0.8556 to 0.9412 and 0.9430 for VoxelMorph and the proposed architecture, respectively. The results show that both architectures perform well in our dataset and the proposed architecture outperforms VoxelMorph in terms of registration accuracy.

Keywords

• cbct
• ct
• unsupervised learning
• deformable image registration
• deep learning