Graph-Based Slice-to-Volume Deformable Registration

Deformable image registration is a fundamental problem in computer vision and medical image computing. In this paper we investigate the use of graphical models in the context of a particular type of image registration problem, known as slice-to-volume registration. We introduce a scalable, modular and flexible formulation that can accommodate low-rank and high order terms, that simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The proposed framework is instantiated into different variants seeking either a compromise between computational efficiency (soft plane selection constraints and approximate definition of the data similarity terms through pair-wise components) or exact definition of the data terms and the constraints on the plane selection. Simulated and real-data in the context of ultrasound and magnetic resonance registration (where both framework instantiations as well as different optimization strategies are considered) demonstrate the potentials of our method.

[1]  Pushmeet Kohli,et al.  Higher-Order Models in Computer Vision , 2012 .

[2]  Ullrich Köthe,et al.  The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models , 2012, ECCV.

[3]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[4]  Michael I. Jordan,et al.  Loopy Belief Propagation for Approximate Inference: An Empirical Study , 1999, UAI.

[5]  Nikos Paragios,et al.  Non-rigid 2 D-3 D Medical Image Registration using Markov Random Fields , 2017 .

[6]  David J. Hawkes,et al.  Correction of misaligned slices in multi-slice MR cardiac examinations by using slice-to-volume registration , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[7]  Nikos Paragios,et al.  Non-rigid 2D-3D Medical Image Registration Using Markov Random Fields , 2013, MICCAI.

[8]  Nikos Paragios,et al.  Rigid Slice-To-Volume Medical Image Registration Through Markov Random Fields , 2016, MCV/BAMBI@MICCAI.

[9]  Caroline Petitjean,et al.  A review of 3 D / 2 D registration methods for image-guided interventions , 2016 .

[10]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[11]  Stefan Heldmann,et al.  Fast intra-operative non-linear registration of 3D-CT to tracked, selected 2D-ultrasound slices , 2011, Medical Imaging.

[12]  Nikos Komodakis,et al.  Discrete Visual Perception , 2014, 2014 22nd International Conference on Pattern Recognition.

[13]  Jing Ren,et al.  Dynamic 2D Ultrasound and 3D CT Image Registration of the Beating Heart , 2009, IEEE Transactions on Medical Imaging.

[14]  Sebastian Nowozin,et al.  A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Pushmeet Kohli,et al.  Exact inference in multi-label CRFs with higher order cliques , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Nikos Komodakis,et al.  (Hyper)-graphical models in biomedical image analysis , 2016, Medical Image Anal..

[17]  Nassir Navab,et al.  Approximated Curvature Penalty in Non-rigid Registration Using Pairwise MRFs , 2009, ISVC.

[18]  Nikos Paragios,et al.  Slice-to-volume deformable registration: efficient one-shot consensus between plane selection and in-plane deformation , 2015, International Journal of Computer Assisted Radiology and Surgery.

[19]  Nikos Komodakis,et al.  A Framework for Efficient Structured Max-Margin Learning of High-Order MRF Models , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  C R Meyer,et al.  Motion correction in fMRI via registration of individual slices into an anatomical volume , 1999, Magnetic resonance in medicine.

[21]  Nikos Komodakis,et al.  Fast, Approximately Optimal Solutions for Single and Dynamic MRFs , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Nikos Paragios,et al.  Slice-to-volume medical image registration: a survey , 2017, Medical Image Anal..

[23]  Kemal Tuncali,et al.  Multi-slice-to-volume registration for MRI-guided transperineal prostate biopsy , 2015, International Journal of Computer Assisted Radiology and Surgery.

[24]  Peng Li,et al.  Correlating 2D histological slice with 3D MRI image volume using smart phone as an interactive tool for muscle study , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Johan H. C. Reiber,et al.  Sparse Registration for Three-Dimensional Stress Echocardiography , 2008, IEEE Transactions on Medical Imaging.

[26]  Ying Sun,et al.  A Review of Recent Advances in Registration Techniques Applied to Minimally Invasive Therapy , 2013, IEEE Transactions on Multimedia.

[27]  Ben Glocker,et al.  Simultaneous Geometric - Iconic Registration , 2010, MICCAI.

[28]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.

[29]  Daniel Rueckert,et al.  Diffusion tensor imaging (DTI) of the brain in moving subjects: Application to in‐utero fetal and ex‐utero studies , 2009, Magnetic resonance in medicine.

[30]  Sang Uk Lee,et al.  Nonrigid Image Registration Using DynamicHigher-Order MRF Model , 2008, ECCV.

[31]  D. Collins,et al.  Online database of clinical MR and ultrasound images of brain tumors. , 2012, Medical physics.

[32]  Nikos Komodakis,et al.  MRF Energy Minimization and Beyond via Dual Decomposition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Colin Studholme,et al.  Cascaded slice to volume registration for moving fetal FMRI , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[34]  X. Jin.  Factor graphs and the Sum-Product Algorithm , 2002 .

[35]  Nikos Komodakis,et al.  Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey , 2013, Comput. Vis. Image Underst..

[36]  Bostjan Likar,et al.  A review of 3D/2D registration methods for image-guided interventions , 2012, Medical Image Anal..

[37]  Nikos Paragios,et al.  Discriminative Parameter Estimation for Random Walks Segmentation , 2013, MICCAI.

[38]  David L Wilson,et al.  Automatic 3D registration for interventional MRI-guided treatment of prostate cancer. , 2002, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[39]  Ben Glocker,et al.  Deformable medical image registration: setting the state of the art with discrete methods. , 2011, Annual review of biomedical engineering.

[40]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[41]  Carl-Fredrik Westin,et al.  Towards real time 2D to 3D registration for ultrasound-guided endoscopic and laparoscopic procedures , 2009, International Journal of Computer Assisted Radiology and Surgery.

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

[43]  J. Schnabel,et al.  Correction of misaligned slices in multi-slice cardiovascular magnetic resonance using slice-to-volume registration , 2008, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.

[44]  Ben Glocker,et al.  Random fields for image registration , 2011 .

[45]  Frithjof Kruggel,et al.  Slice-to-Volume Nonrigid Registration of Histological Sections to MR Images of the Human Brain , 2010, Anatomy research international.

[46]  Nikos Paragios,et al.  Implicit planar and in-plane deformable mapping in medical images through high order graphs , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[47]  Nassir Navab,et al.  Linear intensity-based image registration by Markov random fields and discrete optimization , 2010, Medical Image Anal..

[48]  J. Kettenbach,et al.  Rigid 2D/3D slice-to-volume registration and its application on fluoroscopic CT images. , 2006, Medical physics.

[49]  Václav Hlavác,et al.  Efficient MRF Deformation Model for Non-Rigid Image Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.