DIRBoost-An algorithm for boosting deformable image registration: Application to lung CT intra-subject registration

We introduce a boosting algorithm to improve on existing methods for deformable image registration (DIR). The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well known in the field of machine learning. DIRBoost utilizes a method for automatic registration error detection to obtain estimates of local registration quality. All areas detected as erroneously registered are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We validated the DIRBoost algorithm on three different DIR methods (ANTS gSyn, NiftyReg, and DROP) on three independent reference datasets of pulmonary image scan pairs. DIRBoost reduced registration errors significantly and consistently on all reference datasets for each DIR algorithm, yielding an improvement of the registration accuracy by 5-34% depending on the dataset and the registration algorithm employed.

[1]  Josien P. W. Pluim,et al.  On Combining Algorithms for Deformable Image Registration , 2012, WBIR.

[2]  William M. Wells,et al.  Bayesian characterization of uncertainty in intra-subject non-rigid registration , 2013, Medical Image Anal..

[3]  Brian B. Avants,et al.  From label fusion to correspondence fusion: A new approach to unbiased groupwise registration , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Nassir Navab,et al.  Dense image registration through MRFs and efficient linear programming , 2008, Medical Image Anal..

[5]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[6]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[7]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[8]  Thomas Guerrero,et al.  A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive , 2013, Physics in medicine and biology.

[9]  Josien P. W. Pluim,et al.  Supervised quality assessment of medical image registration: Application to intra-patient CT lung registration , 2012, Medical Image Anal..

[10]  Clayton D. Scott,et al.  Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty , 2012, WBIR.

[11]  D. Xu,et al.  Nodule management protocol of the NELSON randomised lung cancer screening trial. , 2006, Lung cancer.

[12]  Sébastien Ourselin,et al.  Fast free-form deformation using graphics processing units , 2010, Comput. Methods Programs Biomed..

[13]  Paul A. Viola,et al.  Boosting Image Retrieval , 2004, International Journal of Computer Vision.

[14]  N. Komodakis,et al.  Non-rigid Registration using Discrete MRFs: Application to Thoracic CT Images , 2010 .

[15]  Gunnar Rätsch,et al.  An Introduction to Boosting and Leveraging , 2002, Machine Learning Summer School.

[16]  Max A. Viergever,et al.  Semi-automatic construction of reference standards for evaluation of image registration , 2011, Medical Image Anal..

[17]  Benoit M. Dawant,et al.  Automatic Detection of the Magnitude and Spatial Location of Error in Non-rigid Registration , 2012, WBIR.

[18]  Josien P. W. Pluim,et al.  DIRBoost: An algorithm for boosting deformable image registration , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[19]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[20]  Marc Modat,et al.  Lung registration using the NiftyReg package , 2010 .

[21]  Eric A. Hoffman,et al.  Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images , 2001, IEEE Transactions on Medical Imaging.

[22]  Brian B. Avants,et al.  Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge , 2011, IEEE Transactions on Medical Imaging.