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Methodological

Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury

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Pages 1458-1468 | Received 12 Mar 2016, Accepted 04 Aug 2016, Published online: 11 Nov 2016

References

  • Bigler ED, Abildskov TJ, Petrie J, Farrer TJ, Dennis M, Simic N, Taylor HG, Rubin KH, Vannatta K, Gerhardt CA, Stancin T, Owen Yeates K. Heterogeneity of brain lesions in pediatric traumatic brain injury. Neuropsychology 2013;27:438–451.
  • Smitherman E, Hernandez A, Stavinoha PL, Huang R, Kernie SG, Diaz-Arrastia R, Miles DK. Predicting outcome after pediatric traumatic brain injury by early magnetic resonance imaging lesion location and volume. Journal of Neurotrauma 2016;33:35–48.
  • Marquez de la Plata C, Ardelean A, Koovakkattu D, Srinivasan P, Miller A, Phuong V, Harper C, Moore C, Whittemore A, Madden C, Diaz-Arrastia R, Devous M, Sr. Magnetic resonance imaging of diffuse axonal injury: Quantitative assessment of white matter lesion volume. Journal of Neurotrauma 2007;24:591–598.
  • Moen KG, Brezova V, Skandsen T, Håberg AK, Folvik M, Vik A. ‘Traumatic axonal injury: The prognostic value of lesion load in corpus callosum, brain stem, and thalamus in different magnetic resonance imaging sequences. Journal of Neurotrauma 2014;31:1486–1496.
  • Ding K, Marquez de la Plata C, Wang JY, Mumphrey M, Moore C, Harper C, Madden CJ, McColl R, Whittemore A, Devous MD, Diaz-Arrastia R. Cerebral atrophy after traumatic white matter injury: Correlation with acute neuroimaging and outcome. Journal of Neurotrauma 2008;25:1433–1440.
  • Pierallini A, Pantano P, Fantozzi LM, Bonamini M, Vichi R, Zylberman R, Pisarri F, Colonnese C, Bozzao L. Correlation between MRI findings and long-term outcome in patients with severe brain trauma. Neuroradiology 2000;42:860–867.
  • Weiss N, Galanaud D, Carpentier A, Tezenas de Montcel S, Naccache L, Coriat P, Puybasset L. A combined clinical and MRI approach for outcome assessment of traumatic head injured comatose patients. Journal of Neurology 2008;255:217–223.
  • Levin HS, Williams D, Crofford MJ, High WM, Jr, Eisenberg HM, Amparo EG, Guinto FC, Jr, Kalisky Z, Handel SF, Goldman AM. Relationship of depth of brain lesions to consciousness and outcome after closed head injury. Journal of Neurosurgery 1988;69:861–866.
  • Breiman L. Random forests. Machine Learning 2001;1:5–32.
  • Yi Z, Criminisi A, Shotton J, Blake A. Discriminative, semantic segmentation of brain tissue in MR images. Medical image computing and computer-assisted intervention 2009;12:558–565.
  • Viola P, Jones M, Snow D. Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision 2005;63:153–161.
  • Geremia E, Clatz O, Menze BH, Konukoglu E, Criminisi A, Ayache N. Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage 2011;57:378–390.
  • Pustina D, Coslett HB, Turkeltaub PE, Tustison N, Schwartz MF, Avants B. Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis. Human Brain Mapping 2016;37:1405–1421.
  • Geremia E, Menze BH, Ayache N. Spatial decision forests for glioma segmentation in multi-channel MR images. Proceedings of MICCAI-BRATS 2012 October 1; Nice, France.
  • Bauer S, Fejes T, Slotboom J, Wiest R, Nolte L-P, Reyes M. Segmentation of brain tumor images based on integrated hierarchical classification and regularization. Proceedings of MICCAI-BRATS 2012 October 1; Nice, France. p 10–13.
  • Zikic D, Glocker B, Konukoglu E, Shotton J, Criminisi A, Ye DH, Demiralp C, Thomas OM, Das T, Jena R, Price SJ. Context-sensitive classification forests for segmentation of brain tumor tissues. Proceedings of MICCAI-BRATS 2012 October 1; Nice, France. p 1–9.
  • Tustison NJ, Shrinidhi KL, Wintermark M, Durst CR, Kandel BM, Gee JC, Grossman, MC, Avants BB. Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 2015;13:209–225.
  • Schapire R. The strength of weak learnability. Machine Learning 1990;5:197–227.
  • Freund Y, Schapire R. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 1997;55:119–139.
  • Ho TK. Random decision forests. Proceedings of the Third International Conference on document analysis and recognition. 1995;1:278–282.
  • Amit Y, Geman D. Shape quantization and recognition with randomized trees. Neural Computation 1997;9:1545–1588.
  • Avants BB, Tustison NJ, Stauffer M, Song G, Wu B, Gee JC. The insight ToolKit image registration framework. Frontiers in Neuroinformatics 2014;8:44.
  • Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006;31:1116–1128.
  • Tustison NJ, Cook PA, Klein A, Song G, Das SR, Duda JT, Kandel BM, van Strien N, Stone JR, Gee JC, Avants BB. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage 2014;99:166–179.
  • Landman BA, Huang AJ, Gifford A, Vikram DS, Lim IAL, Farrell JAD, Bogovic JA, Hua J, Chen M, Jarso S, Smith SA, Joel S, Mori S, Pekar JJ, Barker PB, Prince JL, van Zijl PCM. Multi-parametric neuroimaging reproducibility: A 3-T resource study. Neuroimage 2011;54:2854–2866.
  • Avants BB, Yushkevich P, Pluta J, Minkoff D, Korczykowski M, Detre J, Gee JC. The optimal template effect in hippocampus studies of diseased populations. Neuroimage 2010;49:2457–2466.
  • Neema M, Guss ZD, Stankiewicz JM, Arora A, Healy BC, Bakshi R. Normal findings on brain fluid-attenuated inversion recovery MR images at 3T. AJNR American Journal of Neuroradiology 2009;30:911–916.
  • Manjón JV, Coupé P, Martí-Bonmatí L, Collins DL, Robles M. Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging 2010;31:192–203.
  • Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC. N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging 2010;29:1310–1320.
  • Nyúl LG, Udupa JK, Zhang X. New variants of a method of MRI scale standardization. IEEE Transactions on Medical Imaging 2000;19:143–150.
  • Avants BB, Tustison NJ, Wu J, Cook PA, Gee JC. An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics 2011;9:381–400.
  • Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M-A, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp Ç, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin H-C, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K. The multimodal brain tumor image segmentation nenchmark (BRATS). IEEE Transactions on Medical Imaging 2015;34:1993–2024.
  • Maurer CR, Rensheng Q, Raghavan V. A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003;25:265–270.
  • Anbeek P, Vincken KL, van Osch MJP, RHC Bisschops, van der Grond J. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 2004;21:1037–1044.
  • Tustison NJ, Avants BB. Explicit B-spline regularization in diffeomorphic image registration. Frontiers in Neuroinformatics 2013;7:39.
  • Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 2011;54:2033–2044.
  • García-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Medical Image Analysis 2013;17:1–18.
  • Liaw A, Wiener M. Classification and regression by random forest. R News 2002;2/3:18–22.
  • Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-analysis. BMJ 2010;341:c3666.
  • Kloppenborg RP, Nederkoorn PJ, Geerlings MI, van den Berg E. Presence and progression of white matter hyperintensities and cognition: A meta-analysis. Neurology 2014;82:2127–2138.
  • Ginneken BV, Heimann T, Styner M. 3D segmentation in the clinic: A grand challenge. Medical image computing and computer-assisted intervention workshop proceedings2007 October 29; Brisbaine, Australia.:7–15.

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