ITM Web Conf.
Volume 47, 20222022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|Number of page(s)||8|
|Section||Algorithm Optimization and Application|
|Published online||23 June 2022|
VoxelMorph++: a convolutional neural network architecture for unsupervised CBCT to CT deformable image registration
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing ; 100083, China
* Corresponding author: email@example.com
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.
Key words: CBCT / CT / Unsupervised learning / Deformable image registration / Deep learning
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.