3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. (September 2021)

• Record Type: Journal Article
• Authors: van Eijnatten, Maureen; Rundo, Leonardo; Batenburg, K. Joost; Lucka, Felix; Beddowes, Emma; Caldas, Carlos; Gallagher, Ferdia A.; Sala, Evis; Schönlieb, Carola-Bibiane; Woitek, Ramona
• Is Part Of: Computer methods and programs in biomedicine. Volume 208(2021)
• Publication Date: 2021-09
• Subjects: Convolutional neural networks -- Deformable registration -- Computed tomography -- Abdominopelvic imaging -- Displacement vector fields -- Incremental training; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106261
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Deformable registration approach based on unsupervised deep learning for longitudinal abdominopelvic CT images. Automated removal of CT table and extracorporeal structures aids image registration. Novel incremental training to effectively train a CNN on a small clinical dataset. Simulated deformation fields were generated for training and evaluation. Single-shot 3D CT image registration 300 × faster than NiftyReg. Graphical abstract: Abstract: Background and Objectives: Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography (CT) images remains challenging due to the larger displacements compared to those in brain or prostate Magnetic Resonance Imaging datasets that are typically considered as benchmarks. In this study, we investigate the use of the commonly used unsupervised deep learning framework VoxelMorph for the registration of a longitudinal abdominopelvic CT dataset acquired in patients with bone metastases from breast cancer. Methods: As a pre-processing step, the abdominopelvic CT images were refined by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of the VoxelMorph framework when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images in the longitudinal dataset. This devised training strategy was compared against training on simulated deformations of a single CT volume. A widely used software toolbox for deformable image registration called NiftyReg was used as a benchmark. The evaluations were performed by calculating the Dice Similarity Coefficient (DSC) between manual vertebrae segmentations and the Structural Similarity Index (SSIM). Results: The CT table removal procedure allowed both VoxelMorph and NiftyReg to achieve significantly better registration performance. In a 4-fold cross-validation scheme, the incremental training strategy resulted in better registration performance compared to training on a single volume, with a mean DSC of 0.929 ± 0.037 and 0.883 ± 0.033, and a mean SSIM of 0.984 ± 0.009 and 0.969 ± 0.007, respectively. Although our deformable image registration method did not outperform NiftyReg in terms of DSC ( 0.988 ± 0.003 ) or SSIM ( 0.995 ± 0.002 ), the registrations were approximately 300 times faster. Conclusions: This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.