Slice-to-volume deformable registration: efficient one-shot consensus between plane selection and in-plane deformation
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This paper introduces a novel decomposed graphical model to deal with slice-to-volume registration in the context of medical images and image-guided surgeries.
We present a new non-rigid slice-to-volume registration method whose main contribution is the ability to decouple the plane selection and the in-plane deformation parts of the transformation—through two distinct graphs—toward reducing the complexity of the model while being able to obtain simultaneously the solution for both of them. To this end, the plane selection process is expressed as a local graph-labeling problem endowed with planarity satisfaction constraints, which is then directly linked with the deformable part through the data registration likelihoods. The resulting model is modular with respect to the image metric, can cope with arbitrary in-plane regularization terms and inherits excellent properties in terms of computational efficiency.
The proof of concept for the proposed formulation is done using cardiac MR sequences of a beating heart (an artificially generated 2D temporal sequence is extracted using real data with known ground truth) as well as multimodal brain images involving ultrasound and computed tomography images. We achieve state-of-the-art results while decreasing the computational time when we compare with another method based on similar techniques.
We confirm that graphical models and discrete optimization techniques are suitable to solve non-rigid slice-to-volume registration problems. Moreover, we show that decoupling the graphical model and labeling it using two lower-dimensional label spaces, we can achieve state-of-the-art results while substantially reducing the complexity of our method and moving the approach close to real clinical applications once considered in the context of modern parallel architectures.
KeywordsSlice-to-volume registration 2D–3D registration Discrete optimization Graphical models Markov random fields
Conflict of interest
The authors declare that they have no conflict of interest.
- 2.Bardera A, Feixas M, Boada I, Sbert M (2006) High-dimensional normalized mutual information for image registration using random lines. In: Pluim J, Likar B, Gerritsen F (eds) Biomedical Image Registration, Lecture Notes in Computer Science, vol 4057, Springer, Berlin, Heidelberg, pp 264–271Google Scholar
- 4.Birkfellner W, Hummel J, Wilson E, Cleary K (2008) Tracking devices. In: Image-guided interventions, Springer, pp 23–44Google Scholar
- 6.Dalvi R, Abugharbieh R (2008) Fast feature based multi slice to volume registration using phase congruency. In: Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp 5390–5393Google Scholar
- 7.Ferrante E, Paragios N (2013) Non-rigid 2d–3d medical image registration using Markov random fields. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013, Springer, pp 163–170Google Scholar
- 8.Fuerst B, Wein W, Muller M, Navab N (2014) Automatic ultrasound–MRI registration for neurosurgery using the 2d and 3d lc2 metric. Med Image Anal 18(8):1312–1319. Special Issue on the 2013 Conference on Medical Image Computing and Computer Assisted InterventionGoogle Scholar
- 9.Geman S, Geman D (1984) Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. Pattern Anal Mach Intell IEEE Trans 6:721–741Google Scholar
- 10.Gill S, Abolmaesumi P, Vikal S, Mousavi P, Fichtinger G (2008) Intraoperative prostate tracking with slice-to-volume registration in MRI. In: Proceedings of the 20th International Conference of the Society for Medical Innovation and Technology, pp 154–158Google Scholar
- 12.Kappes JH, Andres B, Hamprecht FA, Schnörr C, Nowozin S, Batra D, Kim S, Kausler BX, Lellmann J, Komodakis N, Rother C (2013) A comparative study of modern inference techniques for discrete energy minimization problem In: CVPR 2013Google Scholar
- 13.Komodakis N (2011) Efficient training for pairwise or higher order crfs via dual decomposition. In: CVPR, pp 1841–1848Google Scholar
- 14.Komodakis N, Tziritas G, Paragios N (2007) Fast, approximately optimal solutions for single and dynamic mrfs. In: Computer vision and pattern recognition, 2007. CVPR’07. IEEE Conference on, pp 1–8Google Scholar
- 15.Kotsas P, Dodd T (2011) A review of methods for 2d/3d registration. WASET Conference Paris, pp 14–16Google Scholar
- 16.Lee K, Kwon D, Yun I, Lee S (2008) Deformable 3d volume registration using efficient mrfs model with decomposed nodes. In: British Machine Vision Conference, pp 1–10Google Scholar
- 17.Mahapatra D, Sun Y (2008) Nonrigid registration of dynamic renal mr images using a saliency based mrf model. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2008, pp 771–779Google Scholar
- 21.Osechinskiy S, Kruggel F (2010) Slice-to-volume nonrigid registration of histological sections to Mr images of the human brain. Anatomy Research International 2011. doi: 10.1155/2011/287860
- 22.Penney G, Blackall J, Hayashi D, Sabharwal T, Adam A, Hawkes D (2001) Overview of an ultrasound to ct or mr registration system for use in thermal ablation of liver metastases. In: Proceedings of Medical Image Understanding and Analysis, Citeseer, vol 1, p 6568Google Scholar
- 25.Xu H, Lasso A, Fedorov A, Tuncali K, Tempany C, Fichtinger G (2014) Multi-slice-to-volume registration for mri-guided transperineal prostate biopsy. Int J Computer Assist Radiol Surg, pp 1–10 CARS. doi: 10.1007/s11548-014-1108-7