Abstract
In many neuroscience and clinical studies, accurate and automatic segmentation of subcortical structures is an important and difficult task. Multi-atlas-based segmentation method has been focus of considerable research due to its promising performance. In general, this technique first employs deformable image registration to construct the correspondences between pre-labeled atlas images and the target image. Then, using the acquired deformation field, labels in the atlas are propagated to the target image space. Obviously, anatomical differences between the target image and atlas images possibly affect the image registration accuracy, thus influencing the final segmentation performance. Another limitation is that the label propagation in most conventional multi-atlas based methods is implemented under a voxel-wise strategy, which cannot adequately utilize the local label information to determine the final label of the target sample. In this paper, we propose a patch-wise label propagation method based on multiple atlases for MR brain segmentation. First, each image patch is characterized by patch intensities and abundant texture features, to increase the accuracy of the patch-based similarity measurement. To determine the weights of the training patches for representing the test sample, a patch-based sparse coding procedure is employed. In the label propagation stage, to alleviate possible misalignment from the registration stage, we perform a patch-wise label propagation strategy in a nonlocal manner to predict the final label for each target sample. To evaluate the proposed segmentation method, we comprehensively implement our proposed method by conducting hippocampus segmentation on the ADNI data set. Experimental results demonstrate the effectiveness of the proposed method and show that the proposed method outperforms two conventional multi-atlas-based methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bertolino, A., Frye, M., Callicott, J.H., Mattay, V.S., Rakow, R., Shelton-Repella, J., et al.: Neuronal pathology in the hippocampal area of patients with bipolar disorder: a study with proton magnetic resonance spectroscopic imaging. Biol. Psychiat. 53(10), 1177–1194 (2003)
Zu, C., Wang, Z., Zhang, D., Shen, D., Wu, G.: Robust multi- atlas label propagation by deep sparse representation. Pattern Recogn. 63, 511–517 (2017)
Zhang, J., Liu, M., An, L., Gao, Y., Shen, D.: Alzheimer’s Disease diagnosis using landmark-based features from longitudinal structural MR images. IEEE J. Biomed. Health Inform. 21(6), 1607–1616 (2017)
Wang, Y., Zhang, P., An, L., Ma, G., Kang, J., Wu, X., et al.: Predicting standard-dose pet image from low-dose pet and multimodal MR images using mapping-based sparse representation. Phys. Med. Biol. 61(2), 791–812 (2016)
Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46(3), 726–738 (2009)
Zu, C., Jie, B., Liu, M., Chen, S., Shen, D., Zhang, D.: Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment. Brain Imaging Behav. 10(4), 1148–1159 (2015)
Wang, L., Gao, Y., Feng, S., Li, G., Gilmore, J.H., Lin, W., et al.: Links: learning-based multi-source integration framework for segmentation of infant brain images. Neuroimage 108, 160–172 (2015)
Wang, H., Suh, J.W., Das, S.R., Pluta, J.B.: Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 611–23 (2013)
Hao, Y., Wang, T., Zhang, X., Duan, Y., Yu, C., Jiang, T., et al.: Local label learning (lll) for subcortical structure segmentation: application to hippocampus segmentation. Hum. Brain Mapp. 35(6), 2674–2697 (2014)
Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3d brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1744–1757 (2010)
Darko, Z., Glocker, B., Criminisi, A.: Atlas encoding by randomized forests for efficient label propagation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 66–73. Springer, Berlin (2013)
Zhang, J., Liang, J., Zhao, H.: Local energy pattern for texture classification using self-adaptive quantization thresholds. IEEE Trans Image Process 22(1), 31–42 (2013)
Fan, Y., Shen, D.: Integrated feature extraction and selection for neuroimage classification. In: Proceedings of SPIE—The International Society for Optical Engineering, San Diego, CA, vol. 7259, pp. 155–160 (2009)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J Comput. Syst. Sci. 55(7), 119–139 (1999)
Zhang, J., Liu, M., Shen, D.: Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE Trans. Image Process. 26(10), 4753–4764 (2017)
Gao, Y., Zhang, H., Zhao, X., Yan, S.: Event classification in microblogs via social tracking. ACM Trans. Intell. Syst. Technol. (TIST) 8(3), Article No. 35 (2017)
Wang, Y., Ma, G., An, L., Shi, F., Zhang, P., Lalush, D.S., et al.: Semisupervised tripled dictionary learning for standard-dose PET image prediction using low-dose PET and multimodal MRI. IEEE Trans. Biomed. Eng. 64(3), 569–579 (2017)
Tong, T., Wolz, R., Coupé, P., Hajnal, J.V., Rueckert, D.: Segmentation of mr images via discriminative dictionary learning and sparse coding: application to hippocampus labeling. Neuroimage 76(1), 11–23 (2013)
Feng, S., Li, W., Dai, Y., Gilmore, J.H., Lin, W., Shen, D.: Label: pediatric brain extraction using learning-based meta-algorithm. Neuroimage 62(3), 1975–1986 (2012)
Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., et al.: N4itk: improved n3 bias correction. IEEE Trans. Med. Imaging. 29(6), 1310–1320 (2010)
Madabhushi, A., Udupa, J.K.: New methods of mr image intensity standardization via generalized scale. Med. Phys. 33(9), 3426–3434 (2006)
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
Acknowledgements
This work is supported by the by National Natural Science Foundation of China (NSFC61701324), Science and Technology Department of Sichuan Province 2016JZ0014, and Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201715).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Rights and permissions
About this article
Cite this article
Wang, Y., Zu, C., Ma, Z. et al. Patch-wise label propagation for MR brain segmentation based on multi-atlas images. Multimedia Systems 25, 73–81 (2019). https://doi.org/10.1007/s00530-017-0577-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00530-017-0577-2