Image Features for Brain Lesion Segmentation Using Random Forests

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• 关键词：Ischemic stroke ; Lesion segmentation ; Magnetic resonance imaging ; Brain MR ; MRI ; Random forest ; RDF ; Acute ; Sub ; acute ; Glioma ; Tumor ; ISLES 2015 ; BRATS 2015 ; SISS ; SPES
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9556
• 期：1
• 页码：119-130
• 全文大小：1,629 KB
• 参考文献：1.Astrup, J., Siesjö, B.K., Symon, L.: Thresholds in cerebral ischemia-the ischemic penumbra. Stroke 12(6), 723–725 (1981)CrossRef
  2.Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, Nassir (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013)CrossRef
  3.Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends\({\textregistered }\) Comput. Graph. 7(2–3), 81–227 (2012)
  4.Filippi, M., Rocca, M.A.: MR imaging of multiple sclerosis. Radiology 259(3), 659–681 (2011). http://​www.​ncbi.​nlm.​nih.​gov/​pubmed/​21602503 CrossRef
  5.Ghosh, N., Yuan, X., Turenius, C.I., et al.: Automated core-penumbra quantification in neonatal ischemic brain injury. J. Cereb. Blood Flow Metab. 32(12), 2161–2170 (2012)CrossRef
  6.de Haan, B., Clas, P., Juenger, H., Wilke, M., Karnath, H.O.: Fast semi-automated lesion demarcation in stroke. NeuroImage Clin. 9, 69–74 (2015). http://​www.​sciencedirect.​com/​science/​article/​pii/​S221315821500119​9 CrossRef
  7.Hevia-Montiel, N., Jimenez-Alaniz, J., Medina-Banuelos, V., et al.: Robust nonparametric segmentation of infarct lesion from diffusion-weighted MR images. IEEE EMBS 2007, 2102–2105 (2007)
  8.Joy, J.E., Johnston, R.B.: Multiple Sclerosis: Current Status and Strategies for the Future. National Academies Press, Washington (2001). http://​www.​ncbi.​nlm.​nih.​gov/​books/​NBK222399/​
  9.Kaus, M.R., Warfield, S.K., Nabavi, A., et al.: Automated segmentation of MR images of brain tumors. Radiology 218(2), 586–591 (2001)CrossRef
  10.Kemmling, A., Flottmann, F., Forkert, N.D., et al.: Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization. J. Cereb. Blood Flow Metab. 35(9), 1397–1405 (2015)CrossRef
  11.Krämer, U.M., Solbakk, A.K., Funderud, I., et al.: The role of the lateral prefrontal cortex in inhibitory motor control. Cortex 49(3), 837–849 (2013)CrossRef
  12.La Mantia, L., Di Pietrantonj, C., Rovaris, M., et al.: Interferons-beta versus glatiramer acetate for relapsing-remitting multiple sclerosis. Cochrane Database Syst. Rev. 7, CD009333 (2014). http://​www.​ncbi.​nlm.​nih.​gov/​pubmed/​25062935
  13.Lansberg, M.G., Straka, M., Kemp, S., et al.: MRI profile and response to endovascular reperfusion after stroke (DEFUSE 2): a prospective cohort study. Lancet. Neurol. 11(10), 860–867 (2012). http://​www.​thelancet.​com/​article/​S147444221270203​X/​fulltext CrossRef
  14.Likar, B., Viergever, M.A., Pernus, F.: Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans. Med. Imag. 20(12), 1398–1410 (2001)CrossRef
  15.Maier, O.: MedPy. https://​pypi.​python.​org/​pypi/​MedPy . Accessed 29 March 2015
  16.Maier, O., Wilms, M., et al.: Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. J. Neurosci. Methods 240, 89–100 (2015)CrossRef
  17.Menze, B., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRef
  18.Mishra, N.K., Albers, G.W., Christensen, S., et al.: Comparison of magnetic resonance imaging mismatch criteria to select patients for endovascular stroke therapy. Stroke 45(5), 1369–1374 (2014). http://​stroke.​ahajournals.​org/​content/​45/​5/​1369.​full CrossRef
  19.Mitra, J., Bourgeat, P., Fripp, J., et al.: Lesion segmentation from multimodal MRI using random forest following ischemic stroke. Neuroimage 98, 324–335 (2014)CrossRef
  20.Nyul, L., Udupa, J., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)CrossRef
  21.Olivot, J.M., Mlynash, M., Thijs, V.N., et al.: Optimal Tmax threshold for predicting penumbral tissue in acute stroke. Stroke 40(2), 469–475 (2009). http://​stroke.​ahajournals.​org/​content/​40/​2/​469.​abstract CrossRef
  22.Pedregosa, F., Varoquaux, G., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNet MATH
  23.Polman, C.H., Reingold, S.C., Banwell, B., et al.: Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69(2), 292–302 (2011). http://​www.​pubmedcentral.​nih.​gov/​articlerender.​fcgi?​artid=​3084507&​tool=​pmcentrez&​rendertype=​abstract
  24.Rekik, I., Allassonniere, S., Carpenter, T.K., Wardlaw, J.M.: Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal. Neuroimage Clin. 1(1), 164–178 (2012)CrossRef
  25.Seghier, M.L., Ramlackhansingh, A., Crinion, J., Leff, A.P., Price, C.J.: Lesion identification using unified segmentation-normalisation models and fuzzy clustering. Neuroimage 41(4–3), 1253–1266 (2008)CrossRef
  26.Wilke, M., de Haan, B., Juenger, H., Karnath, H.O.: Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. Neuroimage 56(4), 2038–2046 (2011)CrossRef
  27.Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012)CrossRef
  
• 作者单位：Oskar Maier (18) (19)
  Matthias Wilms (18)
  Heinz Handels (18)
  
  18. Institute of Medical Informatics, Universität zu Lübeck, Lübeck, Germany
  19. Graduate School for Computing in Medicine and Life Sciences, Universität zu Lübeck, Lübeck, Germany
  
• 丛书名：Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
• ISBN：978-3-319-30858-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

From clinical practice as well as research methods arises the need for accurate, reproducible and reliable segmentation of pathological areas from brain MR scans. This paper describes a set of hand-selected, voxel-based image features highly suitable for the tissue discrimination task. Embedded in a random decision forest framework, the proposed method was applied to sub-acute ischemic stroke (ISLES 2015 - SISS), acute ischemic stroke (ISLES 2015 - SPES) and glioma (BRATS 2015) segmentation with only minor adaptation. For all of these three challenges, our generic approach received high ranks, among them a second place. The outcome underlines the robustness of our features for segmentation in brain MR, while simultaneously stressing the necessity for highly specialized solution to achieve state-of-the-art performance.