Abstract
The multi-atlas method is one of the efficient and common automatic labeling method, which uses the prior information provided by expert-labeled images to guide the labeling of the target. However, most multi-atlas-based methods depend on the registration that may not give the correct information during the label propagation. To address the issue, we designed a new automatic labeling method through the hashing retrieval based atlas forest. The proposed method propagates labels without registration to reduce the errors, and constructs a target-oriented learning model to integrate information among the atlases. This method innovates a coarse classification strategy to preprocess the dataset, which retains the integrity of dataset and reduces computing time. Furthermore, the method considers each voxel in the atlas as a sample and encodes these samples with hashing for the fast sample retrieval. In the stage of labeling, the method selects suitable samples through hashing learning and trains atlas forests by integrating the information from the dataset. Then, the trained model is used to predict the labels of the target. Experimental results on two datasets illustrated that the proposed method is promising in the automatic labeling of MR brain images.








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Bauer, S., Wiest, R., Nolte, L. P., and Reyes, M., A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13):R97, 2013.
Burgos, N., Guerreiro, F., McClelland, J., Presles, B., Modat, M., Nill, S., Dearnaley, D., deSouza, N., Oelfke, U., and Knopf, A.-C., Iterative framework for the joint segmentation and ct synthesis of mr images: Application to mri-only radiotherapy treatment planning. Phys. Med. Biol. 62(11):4237–4253, 2017.
Van Der Lijn, F., en Heijer, T., Breteler, M. M. B., and Niessen, W. J., Hippocampus segmentation in mr images using atlas registration, voxel classification, and graph cuts. Neuroimage 43(4):708–720, 2008.
García-Lorenzo, D., Francis, S., Narayanan, S., Arnold, D. L., and Collins, D. L., Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med. Image Anal. 17(1):1–18, 2013.
Iglesias, J. E., Van Leemput, K., Augustinack, J., Insausti, R., Fischl, B., and Reuter, M., Bayesian longitudinal segmentation of hippocampal substructures in brain mri using subject-specific atlases. Neuroimage 141: 542–555, 2016.
Klein, A., and Tourville, J., 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6:171, 2012.
Išgum, I., Benders, M. J., Avants, B., Cardoso, M. J., Counsell, S. J., Gomez, E. F., Gui, L., Hűppi, P. S., Kersbergen, K. J., and Makropoulos, A., Evaluation of automatic neonatal brain segmentation algorithms: The neobrains12 challenge. Med. Image Anal. 20(1):135–151, 2015.
Makropoulos, A., Gousias, I. S., Ledig, C., Aljabar, P., Serag, A., Hajnal, J. V., Edwards, A. D., Counsell, S. J., and Rueckert, D., Automatic whole brain MRI segmentation of the developing neonatal brain. IEEE Trans. Med. Imaging 33(9):1818–1831, 2014.
Rohlfing, T., and Maurer, C. R., Multi-classifier framework for atlas-based image segmentation. Pattern Recognit. Lett. 26(13):2070–2079, 2005.
Vemuri, B. C., Ye, J., Chen, Y., and Leonard, C. M., Image registration via level-set motion: Applications to atlas-based segmentation. Med. Image Anal. 7(1):1–20, 2003.
Suh, J. W., Schaap, M., Lee, A., Do, N., Ahiekpor-Dravi, A., Bai, Y., Choi, G., and Moreau-Gobard, R.: Automatic multi-atlas segmentation using dual registrations. In: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 1284–1287. IEEE, 2013.
Zikic, D., Glocker, B., and Criminisi, A., Encoding atlases by randomized classification forests for efficient multi-atlas label propagation. Med. Image Anal. 18(8):1262–1273, 2014.
Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., and Hammers, A., Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33(1):115–26, 2006.
Romero, J. E., Manjón, J. V., Tohka, J., Coupé, P., and Robles, M., Nabs: Non-local automatic brain hemisphere segmentation. Magn. Reson. Imaging 33(4):474–484, 2015.
Rousseau, F., Habas, P. A., and Studholme, C., A supervised patch-based approach for human brain labeling. IEEE Trans. Med. Imaging 30(10):1852–62, 2011.
Wang, Z., Wolz, R., Tong, T., and Rueckert, D.: Spatially aware patch-based segmentation (saps): An alternative patch-based segmentation framework. In: International Conference on Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging, pp. 93–103, 2012.
Wu, G., Wang, Q., Zhang, D., Nie, F., Huang, H., and Shen, D., A generative probability model of joint label fusion for multi-atlas based brain segmentation. Med. Image Anal. 18(6):881–90, 2014.
Bai, W., Shi, W., O’Regan, D. P., Tong, T., Wang, H., Jamilcopley, S, Peters, N. S., and Rueckert, D., A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: Application to cardiac mr images. IEEE Trans. Med. Imaging 32(7):1302–15, 2013.
Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., and Yushkevich, P. A., Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35(3):611–623, 2013.
Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. V., and Rueckert, D., Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. Neuroimage 46(3):726–738, 2009.
Wang, H., and Yushkevich, P. A., Groupwise segmentation with multi-atlas joint label fusion. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013 16(1):711–718, 2013.
Sabuncu, M. R., Yeo, B. T., Van Leemput, K., Fischl, B., and Golland, P., A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imaging 29(10):1714, 2010.
Tong, T., Wolz, R., Hajnal, J. V., and Rueckert, D.: Segmentation of brain mr images via sparse patch representation. In: MICCAI Workshop on Sparsity Techniques in Medical Imaging (STMI), 2012.
Bai, W., Shi, W., Ledig, C., and Rueckert, D., Multi-atlas segmentation with augmented features for cardiac mr images. Med. Image Anal. 19(1):98–109, 2015.
Hao, Y., Wang, T., Zhang, X., Duan, Y., Yu, C., Jiang, T., and Fan, Y., Initiative Alzheimer’s Disease Neuroimaging. Local label learning (lll) for subcortical structure segmentation: Application to hippocampus segmentation. Hum Brain Mapp. 35(6):2674–97, 2014.
Akselrod-Ballin, A., Galun, M., Gomori, M. J., Basri, R., and Brandt, A.: Atlas guided identification of brain structures by combining 3d segmentation and svm classification, pp. 209–216. Springer, 2006.
Kasiri, K., Kazemi, K., Dehghani, M. J., and Helfroush, M. S.: Atlas-based segmentation of brain mr images using least square support vector machines. In: 2010 2nd International Conference on Image Processing Theory Tools and Applications (IPTA), pp. 306–310. IEEE, 2010.
Zhang, L., Wang, Q., Gao, Y., Wu, G., and Shen, D., Automatic labeling of mr brain images by hierarchical learning of atlas forests. Med. Phys. 43(3):1175, 2016.
Chen, H., Dou, Q., Yu, L., and Heng, P.-A.: Voxresnet: Deep voxelwise residual networks for volumetric brain segmentation. arXiv preprint arXiv:1608.05895, 2016
Cao, L., Li, L., Zheng, J., Fan, X., Yin, F., Shen, H., and Zhang, J., Multi-task neural networks for joint hippocampus segmentation and clinical score regression. Multimed. Tools Appl. 77(22):1–18, 2018.
Huo, J., Wu, J., Cao, J., and Wang, G., Supervoxel based method for multi-atlas segmentation of brain mr images. Neuroimage 175:201–214, 2018.
Quinlan, J. R., Induction of decision trees. Mach. Learn. 1(1):81–106, 1986.
Fonov, V., Pruessner, J., Robles, M., and Collins, D. L.: Nonlocal patch-based label fusion for hippocampus segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 129–136, 2010.
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This research was supported by the National Key R&D Program of China(2017YFC0112804).
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This study was funded by National Key R&D Program of China(2017YFC0112804).
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Liu, H., Xu, L., Song, E. et al. Automatic Labeling of MR Brain Images Through the Hashing Retrieval Based Atlas Forest. J Med Syst 43, 241 (2019). https://doi.org/10.1007/s10916-019-1385-3
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DOI: https://doi.org/10.1007/s10916-019-1385-3