Top

International Journal of Computer Vision

Published in:

11-08-2016

A Discrete MRF Framework for Integrated Multi-Atlas Registration and Segmentation

Authors: Stavros Alchatzidis, Aristeidis Sotiras, Evangelia I. Zacharaki, Nikos Paragios

Published in: International Journal of Computer Vision | Issue 1/2017

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Multi-atlas segmentation has emerged in recent years as a simple yet powerful approach in medical image segmentation. It commonly comprises two steps: (1) a series of pairwise registrations that establish correspondences between a query image and a number of atlases, and (2) the fusion of the available segmentation hypotheses towards labeling objects of interest. In this paper, we introduce a novel approach that solves simultaneously for the underlying segmentation labels and the multi-atlas registration. The proposed approach is formulated as a pairwise Markov Random Field, where registration and segmentation nodes are coupled towards simultaneously recovering all atlas deformations and labeling the query image. The coupling is achieved by promoting the consistency between selected deformed atlas segmentations and the estimated query segmentation. Additional membership fields are estimated, determining the participation of each atlas in labeling each voxel. Inference is performed by using a sequential relaxation scheme. The proposed approach is validated on the IBSR dataset and is compared against standard post-registration label fusion strategies. Promising results demonstrate the potential of our method.

previous article Convolutional Patch Representations for Image Retrieval: An Unsupervised Approach

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Akhondi-Asl, A., & Warfield, S. K. (2013). Simultaneous truth and performance level estimation through fusion of probabilistic segmentations. Transaction on Medical Imaging, 32, 1840.CrossRef

Alchatzidis, S., Sotiras, A., Paragios, N. (2014). Discrete multi atlas segmentation using agreement constraints. In: British Machine Vision Conference. http://hal.inria.fr/hal-01061457.

Artaechevarria, X., Munoz-Barrutia, A., & Ortiz-de Solorzano, C. (2009). Combination strategies in multi-atlas image segmentation: application to brain MR data. Medical Imaging IEEE Transactions on Medical Imaging, 28(8), 1266–1277. doi:10.1109/tmi.2009.2014372.CrossRef

Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851. doi:10.1016/j.neuroimage.2005.02.018.CrossRef

Asman, A. J., & Landman, B. A. (2011). Robust statistical label fusion through consensus level, labeler accuracy, and truth estimation (COLLATE). Medical Imaging IEEE Transactions on Medical Imaging, 30(10), 1779–1794. doi:10.1109/tmi.2011.2147795.CrossRef

Asman, A. J., & Landman, B. A. (2013). Non-local statistical label fusion for multi-atlas segmentation. Medical Image Analysis, 17(2), 194–208. doi:10.1016/j.media.2012.10.002.CrossRef

Asman, A., Smith, S., Reich, D., Landman, B. (2013). Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (Eds.) Medical image computing and computer-assisted intervention MICCAI 2013, lecture notes in computer science, vol. 8149, pp. 759–767. Berlin:Springer, doi:10.1007/978-3-642-40811-3_95.

Breiman, L. (2001). Random Forests. Machine learning, 45(1), 5–32. doi:10.1023/a:1010933404324.MathSciNetMATHCrossRef

Coupé, P., Manjón, J. V., Fonov, V., Pruessner, J., Robles, M., & Collins, D. L. (2011). Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage, 54(2), 940–954. doi:10.1016/j.neuroimage.2010.09.018.CrossRef

Doshi, J., Erus, G., Ou, Y., Gaonkar, B., & Davatzikos, C. (2013). Multi-atlas skull-stripping. Academic radiology, 20(12), 1566–1576.CrossRef

Dowling, J., Fripp, J., Chandra, S., Pluim, J., Lambert, J., Parker, J., Denham, J., Greer, P., Salvado, O. (2011). Fast automatic multi-atlas segmentation of the prostate from 3D MR images. In: Madabhushi, A., Dowling, J., Huisman, H., Barratt, D. (Eds.) Prostate cancer imaging. image analysis and image-guided interventions, lecture notes in computer science, vol. 6963, pp. 10–21. Berlin: Springer. doi:10.1007/978-3-642-23944-1_2.

Fonov, V., Coupé, P., Eskildsen, S., Manjon, J., Collins, L. (2012). Multi-atlas labeling with population-specific template and non-local patch-based label fusion. In: MICCAI 2012 Workshop on multi-atlas labeling. pp. 63–66, https://hal.inria.fr/hal-00739285/

Glocker, B., Sotiras, A., Komodakis, N., & Paragios, N. (2011). Deformable medical image registration: setting the state of the art with discrete methods*. Annual Review of Biomedical Engineering, 13(1), 219–244.CrossRef

Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., & Hammers, A. (2006). Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage, 33(1), 115–126. doi:10.1016/j.neuroimage.2006.05.061.CrossRef

Iglesias, J. E., Sabuncu, M. R., & Van Leemput, K. (2013). A unified framework for cross-modality multi-atlas segmentation of brain MRI. Medical Image Analysis, 17(8), 1181–1191. doi:10.1016/j.media.2013.08.001.CrossRef

Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M. A., & van Ginneken, B. (2009). Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in CT scans. Medical Imaging IEEE Transactions on, 28(7), 1000–1010. doi:10.1109/tmi.2008.2011480.CrossRef

Jorge Cardoso, M., Leung, K., Modat, M., Keihaninejad, S., Cash, D., Barnes, J., et al. (2013). Similarity and truth estimation for propagated segmentations and its application to hippocampal segmentation and brain parcelation. Medical Image Analysis, 17(6), 671–684. doi:10.1016/j.media.2013.02.006.CrossRef

Klein, S., van der Heide, U. A., Lips, I. M., van Vulpen, M., Staring, M., & Pluim, J. P. W. (2008). Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics, 35(4), 1407–1417. doi:10.1118/1.2842076.CrossRef

Komodakis, N., Paragios, N., & Tziritas, G. (2011). MRF energy minimization and beyond via dual decomposition. Pattern Analysis and Machine Intelligence IEEE Transactions on, 33(3), 531–552. doi:10.1109/tpami.2010.108.CrossRef

Kybic, J., & Unser, M. (2003). Fast parametric elastic image registration. Image Processing IEEE Transactions on, 12(11), 1427–1442.CrossRef

Langerak, T. R., van der Heide, U. A., Kotte, A. N. T. J., Viergever, M. A., van Vulpen, M., & Pluim, J. P. W. (2010). Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). Medical Imaging IEEE Transactions on, 29(12), 2000–2008. doi:10.1109/tmi.2010.2057442.CrossRef

Ou, Y., Shen, D., Feldman, M., Tomaszewski, J., Davatzikos, C. (2009). Non-rigid registration between histological and MR images of the prostate: A joint segmentation and registration framework. In: Computer vision and pattern recognition workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on. pp. 125–132. IEEE (Jun 2009), doi:10.1109/cvpr.2009.5204347

Parisot, S., Duffau, H., Chemouny, S., Paragios, N. (2012). Joint tumor segmentation and dense deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (Eds.) Medical image computing and computer-assisted intervention MICCAI 2012, lecture notes in computer science, vol. 7511, pp. 651–658. Berlin: Springer. doi:10.1007/978-3-642-33418-4_80

Rohlfing, T. (2012). Image similarity and tissue overlaps as surrogates for image registration accuracy: Widely used but unreliable. IEEE Transactions on Medical Imaging, 31(2), 153–163. doi:10.1109/tmi.2011.2163944.CrossRef

Rohlfing, T., Brandt, R., Menzel, R., Russakoff, D., & Maurer, C. (2005). Quo vadis, atlas-based segmentation? In J. Suri, D. Wilson, & S. Laxminarayan (Eds.), Handbook of biomedical image analysis (pp. 435–486)., opics in Biomedical Engineering International Book Series New York: Springer.CrossRef

Rohlfing, T., Russakoff, D., Maurer, C. (2003). Expectation maximization strategies for multi-atlas multi-label segmentation. In: Taylor, C., Noble (Eds.) Information processing in medical imaging, lecture notes in computer science, vol. 2732, pp. 210–221. Berlin: Springe. doi:10.1007/978-3-540-45087-0_18

Rousseau, F., Habas, P. A., & Studholme, C. (2011). A supervised patch-based approach for human brain labeling. Medical Imaging IEEE Transactions on, 30(10), 1852–1862. doi:10.1109/tmi.2011.2156806.CrossRef

Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999). Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging, 18, 712–721.CrossRef

Sabuncu, M. R., Yeo, B. T. T., Van Leemput, K., Fischl, B., & Golland, P. (2010). A generative model for image segmentation based on label fusion. Medical Imaging IEEE Transactions on, 29(10), 1714–1729. doi:10.1109/tmi.2010.2050897.CrossRef

Sdika, M. (2010). Combining atlas based segmentation and intensity classification with nearest neighbor transform and accuracy weighted vote. Medical Image Analysis, 14(2), 219–226.CrossRef

Seghier, M. L., Ramlackhansingh, A., Crinion, J., Leff, A. P., & Price, C. J. (2008). Lesion identification using unified segmentation-normalisation models and fuzzy clustering. NeuroImage, 41(4), 1253–1266. doi:10.1016/j.neuroimage.2008.03.028.CrossRef

Tang, X., Oishi, K., Faria, A. V., Hillis, A. E., Albert, M. S., Mori, S., et al. (2013). Bayesian parameter estimation and segmentation in the multi-atlas random orbit model. PloS one, 8, e65591.CrossRef

van Rikxoort, E. M., Isgum, I., Arzhaeva, Y., Staring, M., Klein, S., Viergever, M. A., et al. (2010). Adaptive local multi-atlas segmentation: Application to the heart and the caudate nucleus. Medical Image Analysis, 14(1), 39–49. doi:10.1016/j.media.2009.10.001.CrossRef

Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., & Yushkevich, P. A. (2013). Multi-atlas segmentation with joint label fusion. Pattern Analysis and Machine Intelligence IEEE Transactions on, 35(3), 611–623. doi:10.1109/tpami.2012.143.CrossRef

Warfield, S. K., Zou, K. H., & Wells, W. M. (2004). Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging, 23(7), 903–921. doi:10.1109/tmi.2004.828354.CrossRef

Warfield, S., Zou, K., Wells, W. (2002). Validation of image segmentation and expert quality with an expectation-maximization algorithm. In: Dohi, T., Kikinis, R. (Eds.) Medical image computing and computer-assisted intervention miccai 2002, lecture notes in computer science, vol. 2488, pp. 298–306. Berlin: Springer. doi:10.1007/3-540-45786-0_37.

Wyatt, P. P. (2003). Noble: MAP MRF joint segmentation and registration of medical images. Medical Image Analysis, 7(4), 539–552. doi:10.1016/s1361-8415(03)00067-7.MathSciNetCrossRef

Xiaohua, C., Brady, M., Lo, J. C., Moore, N. (2005). Simultaneous segmentation and registration of contrast-enhanced breast MRI. In: Christensen, G., Sonka, M. (Eds.) Information processing in medical imaging, lecture notes in computer science, vol. 3565, pp. 126–137. Berlin: Springer. doi:10.1007/11505730_11.

Xiaohua, C., Brady, M., Rueckert, D. (2004). Simultaneous segmentation and registration for medical image. In: Barillot, C., Haynor, D., Hellier, P. (Eds.) Medical image computing and computer-assisted intervention MICCAI 2004, lecture notes in computer science, vol. 3216, pp. 663–670. Berlin: Springer. doi:10.1007/978-3-540-30135-6_81.

Xue, Z., Wong, K., & Wong, S. T. C. (2010). Joint registration and segmentation of serial lung CT images for image-guided lung cancer diagnosis and therapy. Computerized Medical Imaging and Graphics, 34(1), 55–60. doi:10.1016/j.compmedimag.2009.05.007.CrossRef

Title: A Discrete MRF Framework for Integrated Multi-Atlas Registration and Segmentation
Authors: Stavros Alchatzidis
Aristeidis Sotiras
Evangelia I. Zacharaki
Nikos Paragios
Publication date: 11-08-2016
Publisher: Springer US
Published in: International Journal of Computer Vision / Issue 1/2017
Print ISSN: 0920-5691
Electronic ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-016-0925-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 1/2017

Hollywood 3D: What are the Best 3D Features for Action Recognition?

Learning a Distance Metric from Relative Comparisons between Quadruplets of Images

Appreciation to IJCV Reviewers of 2016

Convolutional Patch Representations for Image Retrieval: An Unsupervised Approach

A Unified Framework for Compositional Fitting of Active Appearance Models

Image Annotation by Propagating Labels from Semantic Neighbourhoods

Premium Partner