publication . Preprint . Other literature type . 2018

Limited One-time Sampling Irregularity Map (LOTS-IM): Automatic Unsupervised Quantitative Assessment of White Matter Hyperintensities in Structural Brain Magnetic Resonance Images

Muhammad Febrian Rachmadi; Maria del C. Valdés-Hernández; Hongwei Li; Ricardo Guerrero; Rozanna Meijboom; Stewart Wiseman; Adam Waldman; Jianguo Zhang; Daniel Rueckert; Taku Komura;
Open Access English
  • Published: 31 May 2018
  • Publisher: Cold Spring Harbor Laboratory
Abstract
<jats:title>Abstract</jats:title><jats:p>We present a complete study of limited one-time sampling irregularity map (LOTS-IM), a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), including its application and evaluation for quantitative assessment of white matter hyperintensities (WMH) of presumed vascular origin and assessing multiple sclerosis (MS) lesion progression. LOTS-IM is unique compared to similar other methods because it yields irregularity map (IM) which represents WMH as irregularity values, not probability values, and retains the original MRI’s texture information. We tested and compared...
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
22 references, page 1 of 2

Bellini, R., Kleiman, Y., Cohen-Or, D., 2016. Timevarying weathering in texture space. ACM Transactions on Graphics (TOG) 35 (4), 141.

Dice, L. R., 1945. Measures of the amount of ecologic association between species. Ecology 26 (3), 297{302. [OpenAIRE]

Fazekas, F., Chawluk, J. B., Alavi, A., Hurtig, H. I., Zimmerman, R. A., 1987. Mr signal abnormalities at 1.5 t in alzheimer's dementia and normal aging. American journal of roentgenology 149 (2), 351{356. [OpenAIRE]

Guerrero, R., Qin, C., Oktay, O., Bowles, C., Chen, L., Joules, R., Wolz, R., Valdes-Hernandez, M., Dickie, D., Wardlaw, J., et al., 2018. White matter hyperintensity and stroke lesion segmentation and di erentiation using convolutional neural networks. NeuroImage: Clinical 17, 918{934. [OpenAIRE]

Hernandez, M. d. C. V., Morris, Z., Dickie, D. A., Royle, N. A., Maniega, S. M., Aribisala, B. S., Bastin, M. E., Deary, I. J., Wardlaw, J. M., 2013. Close correlation between quantitative and qualitative assessments of white matter lesions. Neuroepidemiology 40 (1), 13{22.

Ithapu, V., Singh, V., Lindner, C., Austin, B. P., Hinrichs, C., Carlsson, C. M., Bendlin, B. B., Johnson, S. C., 2014. Extracting and summarizing white matter hyperintensities using supervised segmentation methods in alzheimer's disease risk and aging studies. Human brain mapping 35 (8), 4219{4235. [OpenAIRE]

Jenkinson, M., Bannister, P., Brady, M., Smith, S., 2002. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17 (2), 825{841.

Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., Glocker, B., 2017. E cient multi-scale 3d fCNNg with fully connected fCRFg for accurate brain lesion segmentation. Medical Image Analysis 36, 61 { 78.

Li, H., Jiang, G., Wang, R., Zhang, J., Wang, Z., Zheng, W.-S., Menze, B., 2018. Fully convolutional network ensembles for white matter hyperintensities segmentation in mr images. arXiv preprint arXiv:1802.05203.

Longstreth, W., Manolio, T. A., Arnold, A., Burke, G. L., Bryan, N., Jungreis, C. A., Enright, P. L., O'Leary, D., Fried, L., Group, C. H. S. C. R., et al., 1996. Clinical correlates of white matter ndings on cranial magnetic resonance imaging of 3301 elderly people the cardiovascular health study. Stroke 27 (8), 1274{1282. [OpenAIRE]

Lutkenho , E. S., Rosenberg, M., Chiang, J., Zhang, K., Pickard, J. D., Owen, A. M., Monti, M. M., 2014. Optimized brain extraction for pathological brains (optibet). PloS one 9 (12), e115551.

Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., Beckett, L., 2005. The alzheimer's disease neuroimaging initiative. Neuroimaging Clinics of North America 15 (4), 869{877.

Myers, J. L., Well, A., Lorch, R. F., 2010. Research design and statistical analysis. Routledge.

Rachmadi, M. F., Valdes-Hernandez, M. d. C., Agan, M. L. F., Di Perri, C., Komura, T., Initiative, A. D. N., et al., 2018. Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain mri with none or mild vascular pathology. Computerized Medical Imaging and Graphics 66, 28{43.

Rachmadi, M. F., Valdes-Hernandez, M. d. C., Agan, M. L. F., Komura, T., 2017a. Deep learning vs. conventional machine learning: Pilot study of wmh segmentation in brain mri with absence or mild vascular pathology. Journal of Imaging 3 (4), 66.

22 references, page 1 of 2
Related research
Abstract
<jats:title>Abstract</jats:title><jats:p>We present a complete study of limited one-time sampling irregularity map (LOTS-IM), a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), including its application and evaluation for quantitative assessment of white matter hyperintensities (WMH) of presumed vascular origin and assessing multiple sclerosis (MS) lesion progression. LOTS-IM is unique compared to similar other methods because it yields irregularity map (IM) which represents WMH as irregularity values, not probability values, and retains the original MRI’s texture information. We tested and compared...
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
22 references, page 1 of 2

Bellini, R., Kleiman, Y., Cohen-Or, D., 2016. Timevarying weathering in texture space. ACM Transactions on Graphics (TOG) 35 (4), 141.

Dice, L. R., 1945. Measures of the amount of ecologic association between species. Ecology 26 (3), 297{302. [OpenAIRE]

Fazekas, F., Chawluk, J. B., Alavi, A., Hurtig, H. I., Zimmerman, R. A., 1987. Mr signal abnormalities at 1.5 t in alzheimer's dementia and normal aging. American journal of roentgenology 149 (2), 351{356. [OpenAIRE]

Guerrero, R., Qin, C., Oktay, O., Bowles, C., Chen, L., Joules, R., Wolz, R., Valdes-Hernandez, M., Dickie, D., Wardlaw, J., et al., 2018. White matter hyperintensity and stroke lesion segmentation and di erentiation using convolutional neural networks. NeuroImage: Clinical 17, 918{934. [OpenAIRE]

Hernandez, M. d. C. V., Morris, Z., Dickie, D. A., Royle, N. A., Maniega, S. M., Aribisala, B. S., Bastin, M. E., Deary, I. J., Wardlaw, J. M., 2013. Close correlation between quantitative and qualitative assessments of white matter lesions. Neuroepidemiology 40 (1), 13{22.

Ithapu, V., Singh, V., Lindner, C., Austin, B. P., Hinrichs, C., Carlsson, C. M., Bendlin, B. B., Johnson, S. C., 2014. Extracting and summarizing white matter hyperintensities using supervised segmentation methods in alzheimer's disease risk and aging studies. Human brain mapping 35 (8), 4219{4235. [OpenAIRE]

Jenkinson, M., Bannister, P., Brady, M., Smith, S., 2002. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17 (2), 825{841.

Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., Glocker, B., 2017. E cient multi-scale 3d fCNNg with fully connected fCRFg for accurate brain lesion segmentation. Medical Image Analysis 36, 61 { 78.

Li, H., Jiang, G., Wang, R., Zhang, J., Wang, Z., Zheng, W.-S., Menze, B., 2018. Fully convolutional network ensembles for white matter hyperintensities segmentation in mr images. arXiv preprint arXiv:1802.05203.

Longstreth, W., Manolio, T. A., Arnold, A., Burke, G. L., Bryan, N., Jungreis, C. A., Enright, P. L., O'Leary, D., Fried, L., Group, C. H. S. C. R., et al., 1996. Clinical correlates of white matter ndings on cranial magnetic resonance imaging of 3301 elderly people the cardiovascular health study. Stroke 27 (8), 1274{1282. [OpenAIRE]

Lutkenho , E. S., Rosenberg, M., Chiang, J., Zhang, K., Pickard, J. D., Owen, A. M., Monti, M. M., 2014. Optimized brain extraction for pathological brains (optibet). PloS one 9 (12), e115551.

Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., Beckett, L., 2005. The alzheimer's disease neuroimaging initiative. Neuroimaging Clinics of North America 15 (4), 869{877.

Myers, J. L., Well, A., Lorch, R. F., 2010. Research design and statistical analysis. Routledge.

Rachmadi, M. F., Valdes-Hernandez, M. d. C., Agan, M. L. F., Di Perri, C., Komura, T., Initiative, A. D. N., et al., 2018. Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain mri with none or mild vascular pathology. Computerized Medical Imaging and Graphics 66, 28{43.

Rachmadi, M. F., Valdes-Hernandez, M. d. C., Agan, M. L. F., Komura, T., 2017a. Deep learning vs. conventional machine learning: Pilot study of wmh segmentation in brain mri with absence or mild vascular pathology. Journal of Imaging 3 (4), 66.

22 references, page 1 of 2
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