Abstract
The magnetic resonance (MR) imaging has become an indispensable tool for diagnosis and study of various brain diseases. To perform an accurate diagnosis of a brain disease and monitor its evolution and treatment outcomes, a neuroradiologist often needs to measure the volume and assess the changes of shapes in specific brain structures along a series of MR images. In general, brain structures are manually delineated by a radiologist and, therefore, they highly dependend on the professional’s skills. In this study, we proposed the construction of a probabilistic atlas consisting of 3D landmark points automatically detected in a set of MR images. Also, we aimed at investigate its applicability to guide the initial positioning of mesh models based on the deformation of the hippocampus in brain MR images. The normalized Dice Similarity Coefficient (DSC) and the Hausdorff Average Distance (HAD) were used for the quantitative performance evaluation of the proposed method. The results showed that the average values obtained by our atlas-based landmark approach were significantly better (DSC = 0.74/0.70, HAD = 0.70/0.73, for left and right hippocampus, respectively) than our previous initial approaches, such as the template-based landmark (DSC = 0.65/0.61, HAD = 0.88/0.91) and the affine transformation (DSC = 0.58/0.53, HAD = 1.10/1.22).
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Notes
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The term “landmark point” or “salient point” refers to an image point that stands locally due to its specific characteristic (e.g., high degree of curvature) and is usually visible to the naked eye. It does not necessarily represent an anatomical reference point that would be marked by an expert, although this is usually the case.
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References
Johnson, K.A., Fox, N.C., Sperling, R.A., Klunk, W.E.: Brain imaging in Alzheimer disease. Cold Spring Harb. Perspect. Med. 2(4), 1–23 (2012)
Shen, K.K., Fripp, J., Mériaudeau, F., Chételat, G., Salvado, O., Bourgeat, P.: Detecting global and local hippocampal shape changes in Alzheimer’s disease using statistical shape models. NeuroImage 59(3), 2155–2166 (2012)
Farag, A.: Deformable Models: Biomedical and Clinical Applications. Springer, Heidelberg (2007). https://doi.org/10.1007/978-0-387-68413-0
Pekar, V., McNutt, T.R., Kaus, M.R.: Automated model-based organ delineation for radiotherapy planning in prostatic region. Int. J. Radiat. Oncol. Biol. Phys. 60(3), 973–980 (2004)
Liu, C.Y., Iglesias, J.E., Tu, Z.: Deformable templates guided discriminative models for robust 3D brain MRI segmentation. NeuroInformatics 11(4), 447–468 (2013)
Ghose, S., Mitra, J., Oliver, A., Marti, R., Llado, X., Freixenet, J., Vilanova, J.C., Sidibe, D., Meriaudeau, F.: A coupled schema of probabilistic atlas and statistical shape and appearance model for 3D prostate segmentation in MR images. In: 19th IEEE International Conference on Image Processing (ICIP), Orlando, FL, pp. 541–544. IEEE (2012)
Ferrari, R.J., Allaire, S., Hope, A., Kim, J., Jaffray, D., Pekar, V.: Detection of point landmarks in 3D medical images via phase congruency model. J. Braz. Comput. Soc. 17(2), 117–132 (2011)
Qazi, A.A., Pekar, V., Kim, J., Xie, J., Breen, S.L., Jaffray, D.A.: Auto-segmentation of normal and target structures in head and neck CT images: A feature-driven model-based approach. Med. Phys. 38(11), 6160–6170 (2011)
Zhang, J., Chen, L., Wang, X., Teng, Z., Brown, A.J., Gillard, J.H., Guan, Q., Chen, S.: Compounding local invariant features and global deformable geometry for medical image registration. PLoS ONE 9(8), e105815 (2014)
Apostolova, L.G., Zarow, C., Biado, K., Hurtz, S., Boccardi, M., Somme, J., Honarpisheh, H., Blanken, A.E., Brook, J., Tung, S., et al.: Relationship between hippocampal atrophy and neuropathology markers: a 7T MRI validation study of the EADC-ADNI harmonized hippocampal segmentation protocol. Alzheimer’s Dement. 11(2), 139–150 (2015)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA, vol. 2, pp. 60–65. IEEE Computer Society (2005)
Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
Nyul, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)
Ourselin, S., Stefanescu, R., Pennec, X.: Robust registration of multi-modal images: towards real-time clinical applications. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 140–147. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45787-9_18
Iglesias, J.E., Liu, C.Y., Thompson, P., Tu, Z.: Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans. Med. Imaging 30(9), 1617–1634 (2011)
Villa-Pinto, C.H., Ferrari, R.J.: Initialization of deformable models in 3D magnetic resonance images guided by automatically detected phase congruency point landmarks. Pattern Recogn. Lett. 79, 1–7 (2016)
Morrone, M.C., Ross, J., Burr, D.C., Owens, R.: Mach bands are phase dependent. Nature 324(6094), 250–253 (1986)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. Pattern Anal. Mach. 24(4), 509–522 (2002)
Vincent, P., Bengio, Y.: Manifold parzen windows. In: Becker, S., Thrun, S., Obermayer, K. (eds.): Advances in Neural Information Processing Systems 15, pp. 849–856. MIT Press (2003)
Lee, S., Wolberg, G., Shin, S.Y.: The ITK Software Guide, 4th edn. Kitware, Inc., Clifton Park (2017)
Lee, S., Wolberg, G., Shin, S.Y.: Scattered data interpolation with multilevel B-splines. IEEE Trans. Vis. Comput. Graph. 3(3), 228–244 (1997)
Henderson, C., Izquierdo, E.: Symmetric stability of low level feature detectors. Pattern Recogn. Lett. 78, 36–40 (2016)
Acknowledgments
The authors would like to thank the São Paulo Research Foundation (FAPESP) (grants no. 2014/11988 0 and 2015/02232 1) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for the financial support of this research.
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Poloni, K.M., Villa Pinto, C.H., da Silveira Souza, B., Ferrari, R.J. (2018). Construction and Application of a Probabilistic Atlas of 3D Landmark Points for Initialization of Hippocampus Mesh Models in Brain MR Images. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_21
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