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References
-
-
1)
-
22. Sdika, M.: ‘Combining atlas based segmentation and intensity classification with nearest neighbor transform and accuracy weighted vote’, Med. Image Anal., 2010, 14, (2), pp. 219–226.
-
2)
-
1. Bueno, G., Musse, O., Heitz, F., et al: ‘Three-dimensional segmentation of anatomical structures in MR images on large data bases’, Magn. Reson. Imaging, 2001, 19, (1), pp. 73–88.
-
3)
-
28. Romero, J.E., Manjón, J.V., Tohka, J., et al: ‘NABS: non-local automatic brain hemisphere segmentation’, Magn. Reson. Imaging, 2015, 33, (4), pp. 474–484.
-
4)
-
16. Wu, G., Kim, M., Sanroma, G., et al: ‘Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition’, NeuroImage, 2015, 106, pp. 34–46.
-
5)
-
35. Kittler, J., Alkoot, F.M.: ‘Sum versus vote fusion in multiple classifier systems’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (1), pp. 110–115.
-
6)
-
4. Crum, W.R., Griffin, L.D., Hill, D.L.G., et al: ‘Zen and the art of medical image registration: correspondence, homology, and quality’, NeuroImage, 2003, 20, (3), pp. 1425–1437.
-
7)
-
30. Tong, T., Wolz, R., Coupé, P., et al: ‘Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling’, NeuroImage, 2013, 76, pp. 11–23.
-
8)
-
31. Wang, L., Shi, F., Gao, Y., et al: ‘Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation’, NeuroImage, 2014, 89, pp. 152–164.
-
9)
-
9. Bai, W., Shi, W., O'Regan, D.P., et al: ‘A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images’, IEEE Trans. Med. Imaging, 2013, 32, (7), pp. 1302–1315.
-
10)
-
37. Hammers, A., Allom, R., Koepp, M.J., et al: ‘Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe’, Hum. Brain Mapp., 2003, 19, (4), pp. 224–247.
-
11)
-
32. Zhang, D., Guo, Q., Wu, G., et al: ‘Sparse patch-based label fusion for multi-atlas segmentation’, in Yap, P.T., Liu, T., Shen, D., Westin, C.F., Shen, L. (Eds): ‘Multimodal brain image analysis’ (Springer Berlin Heidelberg, 2012), (, 7509), pp. 94–102.
-
12)
-
7. Cardoso, M.J., Leung, K., Modat, M., et al: ‘STEPS: similarity and truth estimation for propagated segmentations and its application to hippocampal segmentation and brain parcelation’, Med. Image Anal., 2013, 17, (6), pp. 671–684.
-
13)
-
33. 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. 1262–1273.
-
14)
-
20. Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de-Solorzano, C.: ‘Combination strategies in multi-atlas image segmentation: application to brain MR data’, IEEE Trans. Med. Imaging, 2009, 28, (8), pp. 1266–1277.
-
15)
-
34. Brebisson, A., Montana, G.: ‘Deep neural networks for anatomical brain segmentation’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2015, pp. 20–28.
-
16)
-
8. Aljabar, P., Heckemann, R.A., Hammers, A., et al: ‘Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy’, Neuroimage, 2009, 46, (3), pp. 726–738.
-
17)
-
12. Khan, A.R., Cherbuin, N., Wen, W., et al: ‘Optimal weights for local multi-atlas fusion using supervised learning and dynamic information (SuperDyn): validation on hippocampus segmentation’, NeuroImage, 2011, 56, (1), pp. 126–139.
-
18)
-
2. Klein, S., van der Heide, U.A., Lips, I.M., et al: ‘Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information’, Med. Phys., 2008, 35, (4), pp. 1407–1417.
-
19)
-
23. Rousseau, R., Habas, P.A., Studholme, C.: ‘A supervised patch-based approach for human brain labeling’, IEEE Trans. Med. Imaging, 2011, 30, (10), pp. 1852–1862.
-
20)
-
10. Liao, S., Gao, Y., Lian, J., et al: ‘Sparse patch-based label propagation for accurate prostate localization in CT images’, IEEE Trans. Med. Imaging, 2013, 32, (2), pp. 419–434.
-
21)
-
17. Sanroma, G., Wu, G., Gao, Y., et al: ‘A transversal approach for patch-based label fusion via matrix completion’, Med. Image Anal., 2015, 24, (1), pp. 135–148.
-
22)
-
25. Wang, H., Suh, J.W., Das, S., et al: ‘Regression-based label fusion for multi-atlas segmentation[C]’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 1113–1120.
-
23)
-
14. 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. 98–109.
-
24)
-
26. Coupe, P., Yger, P., Prima, S., et al: ‘An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images’, IEEE Trans. Med. Imaging, 2008, 27, (4), pp. 425–441.
-
25)
-
38. Hammers, A., Chen, C.H., Lemieux, L., et al: ‘Statistical neuroanatomy of the human inferior frontal gyrus and probabilistic atlas in a standard stereotaxic space’, Hum. Brain Mapp., 2007, 28, (1), pp. 34–48.
-
26)
-
36. Wright, J., Yang, A.Y., Ganesh, A., et al: ‘Robust face recognition via sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (2), pp. 210–227.
-
27)
-
21. Isgum, I., Staring, M., Rutten, A., et al: ‘Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in CT scans’, IEEE Trans. Med. Imaging, 2009, 28, (7), pp. 1000–1010.
-
28)
-
13. 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. 308–319.
-
29)
-
11. Coupé, P., Manjón, J.V., Fonov, V., et al: ‘Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation’, NeuroImage, 2011, 54, (2), pp. 940–954.
-
30)
-
27. Buades, A., Coll, B., Morel, J.M.: ‘A review of image denoising algorithms, with a new one’, Multiscale Model. Simul., 2005, 4, (2), pp. 490–530.
-
31)
-
15. Yaqub, M., Javaid, M.K., Cooper, C., et al: ‘Investigation of the role of feature selection and weighted voting in random forests for 3-D volumetric segmentation’, IEEE Trans. Med. Imaging, 2014, 33, (2), pp. 258–271.
-
32)
-
5. 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. 115–126.
-
33)
-
24. Wang, H., Suh, J.W., Das, S.R., et al: ‘Multi-atlas segmentation with joint label fusion’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (3), pp. 611–623.
-
34)
-
6. Joshi, S., Davis, B., Jomier, M., et al: ‘Unbiased diffeomorphic atlas construction for computational anatomy[J]’, NeuroImage, 2004, 23, pp. S151–S160.
-
35)
-
29. Giraud, R., Ta, V.T., Papadakis, N., et al: ‘An Optimized PatchMatch for multi-scale and multi-feature label fusion’, NeuroImage, 2016, 124, pp. 770–782.
-
36)
-
19. Rohlfing, T., Brandt, R., Menzel, R., et al: ‘Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains’, NeuroImage, 2004, 21, (4), pp. 1428–1442.
-
37)
-
18. Iglesias, J.E., Sabuncu, M.R., Aganj, I., et al: ‘An algorithm for optimal fusion of atlases with different labeling protocols’, NeuroImage, 2015, 106, pp. 451–463.
-
38)
-
3. Svarer, C., Madsen, K., Hasselbalch, S.G., et al: ‘MR-based automatic delineation of volumes of interest in human brain PET images using probability maps’, Neuroimage, 2005, 24, (4), pp. 969–979.
-
39)
-
39. 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. 196–205.
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