Pulmonary CT Registration Through Supervised Learning With Convolutional Neural Networks

• Published in: IEEE transactions on medical imaging 2019-05, Vol.38 (5), p.1097-1105
• Main Authors: Eppenhof, Koen A. J., Pluim, Josien P. W.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Biomedical imaging; Computed tomography; Convolutional neural networks; Deformable image registration; Deformation; Formability; Ground truth; Humans; Image registration; Imaging, Three-Dimensional; Lung; Lung - diagnostic imaging; Lungs; machine learning; Neural networks; Neural Networks, Computer; Parameterization; pulmonary CT images; Registration; Strain; Supervised learning; Supervised Machine Learning; Tomography, X-Ray Computed - methods; Training; Transformations
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2018.2878316

Deformable image registration can be time consuming and often needs extensive parameterization to perform well on a specific application. We present a deformable registration method based on a 3-D convolutional neural network, together with a framework for training such a network. The network directly learns transformations between pairs of 3-D images. The network is trained on synthetic random transformations which are applied to a small set of representative images for the desired application. Training, therefore, does not require manually annotated ground truth information on the deformation. The framework for the generation of transformations for training uses a sequence of multiple transformations at different scales that are applied to the image. This way, complex transformations with large displacements can be modeled without folding or tearing images. The methodology is demonstrated on public data sets of inhale-exhale lung CT image pairs which come with landmarks for evaluation of the registration quality. We show that a small training set can be used to train the network, while still allowing generalization to a separate pulmonary CT data set containing data from a different patient group, acquired using a different scanner and scan protocol. This approach results in an accurate and very fast deformable registration method, without a requirement for parameterization at test time or manually annotated data for training.