access icon free Automatic labelling of brain tissues in MR images through spatial indexes based hybrid atlas forest

The multi-atlas-based methods are widely applied in the automatic labelling in magnetic resonance (MR) images. However, most multi-atlas-based methods require that all atlases be registered to the target image accurately to have a correct label propagation. In this study, the authors introduce the term spatial indexes and construct a hybrid atlas forest model to gather the labelling information from all atlases without propagating labels from every single atlas. Furthermore, a new automatic labelling method using the hybrid atlas forest model based on spatial indexes is proposed. In the proposed framework, an atlas is chosen arbitrarily as a reference image and the spatial indexes are constructed on this image space. Then, the samples are selected from all atlases in the dataset based on the spatial indexes to construct a samples pool. Finally, the hybrid atlas forest model will be trained on the samples pool and used to predict the labelling of the target. Experiments are conducted on two public datasets to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method reduces the requirement of strong dependence on precise registration and improve the accuracy of labelling.

Inspec keywords: brain; image registration; medical image processing; biological tissues; biomedical MRI

Other keywords: multiatlas-based methods; MR images; image space; correct label propagation; magnetic resonance images; single atlas; brain tissues; labelling information; spatial index based hybrid atlas forest; automatic labelling method; target image; hybrid atlas forest model; image registration

Subjects: Biology and medical computing; Patient diagnostic methods and instrumentation; Computer vision and image processing techniques; Biomedical magnetic resonance imaging and spectroscopy; Optical, image and video signal processing; Medical magnetic resonance imaging and spectroscopy

References

    1. 1)
      • 22. Shepherd, B.A.: ‘An appraisal of a decision tree approach to image classification’. Eighth Int. Joint Conf. on Artificial Intelligence, San Mateo, CA, USA., 1983, pp. 473475.
    2. 2)
      • 2. Zhou, J., Rajapakse, J.C.: ‘Segmentation of subcortical brain structures using fuzzy templates’, Neuroimage, 2005, 28, (4), p. 915.
    3. 3)
      • 20. Breiman, L.: ‘Random forests’, Mach. Learn., 2001, 45, (1), pp. 532.
    4. 4)
      • 24. Pereira, S., Pinto, A., Oliveira, J., et al: ‘Automatic brain tissue segmentation in MR images using random forests and conditional random fields’, J. Neurosci. Methods, 2016, 270, pp. 111123.
    5. 5)
      • 7. Lotjonen, J.M., Wolz, R., Koikkalainen, J.R., et al: ‘Fast and robust multi-atlas segmentation of brain magnetic resonance images’, Neuroimage, 2010, 49, (3), pp. 23522365.
    6. 6)
      • 15. Hoang Duc, A.K., Modat, M., Leung, K.K., et al: ‘Using manifold learning for atlas selection in multi-atlas segmentation. PLoS One, 2013, 8, (8), p. e70059.
    7. 7)
      • 9. Heckemann, R.A., Hajnal, J.V., Aljabar, P., et al: ‘Automatic anatomical brain MRI segmentation combining label propagation and decision fusion’, Neuroimage, 2006, 33, (1), pp. 115126.
    8. 8)
      • 23. Zikic, D., Glocker, B., Criminisi, A.: ‘Encoding atlases by randomized classification forests for efficient multi-atlas label propagation’, Med. Image Anal., 2014, 18, (8), pp. 12621273.
    9. 9)
      • 28. Sjöberg, C., Ahnesjö, A.: ‘Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures’, Comput. Methods Programs Biomed., 2013, 110, (3), pp. 308319.
    10. 10)
      • 13. Wu, G., Wang, Q., Zhang, D., et al: ‘A generative probability model of joint label fusion for multi-atlas based brain segmentation’, Med. Image Anal., 2014, 18, (6), pp. 881890.
    11. 11)
      • 26. Ma, G., Gao, Y., Wu, G., et al: ‘Nonlocal atlas-guided multi-channel forest learning for human brain labeling’, Med. Phys., 2016, 43, (2), p. 1003.
    12. 12)
      • 17. Bai, W., Shi, W., Ledig, C., et al: ‘Multi-atlas segmentation with augmented features for cardiac MR images’, Med. Image Anal., 2015, 19, (1), pp. 98109.
    13. 13)
      • 6. Lotjonen, J., Koikkalainen, J., Thurfjell, L., et al: ‘Atlas-based registration parameters in segmenting sub-cortical regions from brain mri-images’. 2009 IEEE Int. Symp. on Biomedical Imaging: From Nano to Macro, Boston, MA, USA., 2009, pp. 2124.
    14. 14)
      • 11. Rousseau, F., Habas, P.A., Studholme, C.: ‘A supervised patch-based approach for human brain labeling’, IEEE Trans. Med. Imaging, 2011, 30, (10), pp. 18521862.
    15. 15)
      • 12. Wang, Z., Wolz, R., Tong, T., et al: ‘Spatially aware patch-based segmentation (SAPS): an alternative patch-based segmentation framework’. Int. Conf. on Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging, Berlin, Germany, 2012, pp. 093103.
    16. 16)
      • 25. Zhang, L., Wang, Q., Gao, Y., et al: ‘Automatic labeling of MR brain images by hierarchical learning of atlas forests’, Med. Phys., 2016, 43, (3), pp. 11751186.
    17. 17)
      • 16. Langerak, T.R., Berendsen, F.F., Van Der Heide, U.A., et al: ‘Multiatlas-based segmentation with preregistration atlas selection’, Med. Phys., 2013, 40, (40), pp. 091701091701.
    18. 18)
      • 3. Chupin, M., Hammers, A., Liu, R.S., et al: ‘Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints method and validation’, Neuroimage, 2009, 46, (3), pp. 749761.
    19. 19)
      • 18. Hao, Y., Wang, T., Zhang, X., et al: ‘Local label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation’, Hum. Brain Mapp., 2014, 35, (6), pp. 26742697.
    20. 20)
      • 29. Klein, S., Staring, M., Murphy, K., et al: ‘elastix: A toolbox for intensity-based medical image registration’, IEEE Trans. Med. Imaging, 2010, 29, (1), pp. 196205.
    21. 21)
      • 14. Cao, Y., Yuan, Y., Li, X., et al: ‘Segmenting images by combining selected atlases on manifold’. Int. Conf. on Medical Image Computing & Computer-assisted Intervention, Toronto, Canada, 2011, pp. 272279.
    22. 22)
      • 1. Klein, A., Tourville, J.: ‘101 labeled brain images and a consistent human cortical labeling protocol’, Front. Neurosci., 2012, 6, p. 171.
    23. 23)
      • 21. Quinlan, J.R.: ‘Induction of decision trees’ machine learning’. Data: Goals and General Description of the IN L.EN System, Virginia Beach, VA, USA., 1986, pp. 257264.
    24. 24)
      • 10. Romero, J.E., Manjón, J.V., Tohka, J., et al: ‘NABS: non-local automatic brain hemisphere segmentation’, Magn. Reson. Imaging, 2015, 33, (4), pp. 474484.
    25. 25)
      • 19. Asman, A.J., Bryan, F.W., Smith, S.A., et al: ‘Groupwise multi-atlas segmentation of the spinal cord's internal structure’, Med. Image Anal., 2014, 18, (3), pp. 460471.
    26. 26)
      • 30. Quinlan, J.R.: ‘Induction of decision trees’, Mach. Learn., 1986, 1, (1), pp. 81106.
    27. 27)
      • 4. Khan, A.R., Wang, L., Beg, M.F.: ‘Freesurfer-initiated fully-automated subcortical brain segmentation in MRI using large deformation diffeomorphic metric mapping’, Neuroimage, 2008, 41, (3), pp. 735746.
    28. 28)
      • 5. Jia, H., Yap, P.T., Shen, D.: ‘Iterative multi-atlas-based multi-image segmentation with tree-based registration’, Neuroimage, 2012, 59, (1), pp. 422430.
    29. 29)
      • 8. Vercauteren, T., Pennec, X., Perchant, A., et al: ‘Diffeomorphic demons: efficient non-parametric image registration’, Neuroimage, 2009, 45, (1 Suppl.), pp. S61S72.
    30. 30)
      • 27. Zhang, L., Wang, Q., Gao, Y., et al: ‘Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images’, Neurocomputing, 2016, 229, pp. 312.
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