Abstract
Patch-based segmentation has been shown to be successful in a range of label propagation applications. Performing patch-based segmentation can be seen as a k-nearest neighbour problem as the labelling of each voxel is determined according to the distances to its most similar patches. However, the reliance on a good affine registration given the use of limited search windows is a potential weakness. This paper presents a novel alternative framework which combines the use of kNN search structures such as ball trees and a spatially weighted label fusion scheme to search patches in large regional areas to overcome the problem of limited search windows. Our proposed framework (SAPS) provides an improvement in the Dice metric of the results compared to that of existing patch-based segmentation frameworks.
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Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)
Rousseau, F., Habas, P., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE Transactions on Medical Imaging 30(10), 1852–1862 (2011)
Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Transactions on Medical Imaging 27(4), 425–41 (2008)
Nyúl, L.G., Udupa, J.K.: On standardizing the MR image intensity scale. Magnetic Resonance in Medicine 42(6), 1072–1081 (1999)
Chambolle, A.: An Algorithm for Total Variation Minimization and Applications. Journal of Mathematical Imaging and Vision 20(1), 89–97 (2004)
Omohundro, S.M.: Five Balltree Construction Algorithms. Technical Report 1, International Computer Science Institute (1989)
Kumar, N., Zhang, L., Nayar, S.K.: What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images? In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 364–378. Springer, Heidelberg (2008)
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)
Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Whitwell, J., Ward, C., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L.G., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C.S., Krueger, G., Ward, H.A., Metzger, G.J., Scott, K.T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J.P., Fleisher, A.S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., Weiner, M.W.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging 27(4), 685–691 (2008)
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Wang, Z., Wolz, R., Tong, T., Rueckert, D. (2013). Spatially Aware Patch-Based Segmentation (SAPS): An Alternative Patch-Based Segmentation Framework. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2012. Lecture Notes in Computer Science, vol 7766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36620-8_10
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DOI: https://doi.org/10.1007/978-3-642-36620-8_10
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