Abstract
Accurate quantification of white matter hyperintensities (WMH) from MRI is a valuable tool for studies on ageing and neurodegeneration. Reliable automatic extraction of WMH biomarkers is challenging, primarily due to their heterogeneous spatial occurrence, their small size and their diffuse nature. In this paper, we present an automatic and accurate method to segment these lesions that is based on the use of neural networks and an overcomplete strategy. The proposed method was compared to other related methods showing competitive and reliable results in two different neurodegenerative datasets.
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Debette, S., Markus, H.S.: The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 341, c3666 (2010)
Filippi, M., Rocca, M.A.: MR imaging of multiple sclerosis. Radiology 259, 659–681 (2011)
Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V.J., Zimmer, C., Hemmer, B., Mühlau, M.: An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage 59, 3774–3783 (2012)
Weiss, N., Rueckert, D., Rao, A.: Multiple sclerosis lesion segmentation using dictionary learning and sparse coding. Med. Image Comput. Comput. Assist. Interv. 16, 735–742 (2013)
Admiraal-Behloul, F., et al.: Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. NeuroImage 28, 607–617 (2005)
Jack, C.R., O’Brien, P.C., Rettman, D.W., Shiung, M.M., Xu, Y., Muthupillai, R., Manduca, A., Avula, R., Erickson, B.J.: FLAIR histogram segmentation for measurement of leukoaraiosis volume. J. Magn. Reson. Imaging 14, 668–676 (2001)
Ithapu, V., Singh, V., Lindner, C., Austin, B.P., Hinrichs, C., Carlsson, C.M., Bendlin, B.B., Johnson, S.C.: Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer’s disease risk and aging studies. Hum. Brain Mapp. 35, 4219–4235 (2014)
Dyrby, T.B., et al.: Segmentation of age-related white matter changes in a clinical multi-center study. NeuroImage 41, 335–345 (2008)
Guizard, N., Coupé, P., Fonov, V., Manjón, J.V., Douglas, A., Collins, D.L.: Rotation-invariant multi-contrast non-local means for MS lesion segmentation. NeuroImage Clin. 8, 376–389 (2015)
Manjón, J.V., Coupé, P., Martí-Bonmatí, L., Collins, D.L., Robles, M.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203 (2010)
Avants, B., Tustison, N., Song, G.: Advanced Normalization Tools: V1.0 (2009)
Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage 26, 839–851 (2005)
Ellis, K.A., et al.: The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int. Psychogeriatr. 21, 1–16 (2009)
Styner, M., Lee, J., Chin, B., Chin, M.S., Commowick, O., Tran, H.-H., Markovic-Plese, S., Jewells, V., Warfield, S.: 3D segmentation in the clinic: a grand challenge II: MS lesion segmentation (2008)
Acknowledgements
This research has been done thanks to the Australian distinguished visiting professor grant and the Spanish “Programa de apoyo a la investigación y desarrollo (PAID-00-15)” of the Universidad Politécnica de Valencia. This study has been carried out with support from the French State, managed by the French National Research Agency in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi imag’In”.
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Manjón, J.V., Coupé, P., Raniga, P., Xia, Y., Fripp, J., Salvado, O. (2016). HIST: HyperIntensity Segmentation Tool. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2016. Lecture Notes in Computer Science(), vol 9993. Springer, Cham. https://doi.org/10.1007/978-3-319-47118-1_12
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DOI: https://doi.org/10.1007/978-3-319-47118-1_12
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