Combining Unsupervised and Supervised Methods for Lesion Segmentation

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• 关键词：White ; matter lesion ; Random decision forest ; Segmentation
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9556
• 期：1
• 页码：45-56
• 全文大小：2,837 KB
• 参考文献：1.Akselrod-Ballin, A., Galun, M., Gomori, J.M., Filippi, M., Valsasina, P., Basri, R., Brandt, A.: Automatic segmentation and classification of multiple sclerosis in multichannel MRI. IEEE Trans. Biomed. Eng. 56(10), 2461–2469 (2009)CrossRef
  2.Criminisi, A., Shotton, J. (eds.): Decision Forests for Computer Vision and Medical Image Analysis. Springer, London (2013)
  3.García-Lorenzo, D., Francis, S., Narayanan, S., Arnold, D.L., Collins, D.L.: Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med. Image Anal. 17(1), 1–18 (2013)CrossRef
  4.García-Lorenzo, D., Prima, S., Collins, L., Arnold, D.L., Morrissey, S.P., Barillot, C.: Combining robust expectation maximization and mean shift algorithms for multiple sclerosis brain segmentation. In: Proceedings of MICCAI Workshop on Medical Image Analysis on Multiple Sclerosis (MIAMS 2008), pp. 82–91 (2008)
  5.Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57(2), 378–390 (2011)CrossRef
  6.Iglesias, J., 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)CrossRef
  7.Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRef
  8.Neykov, N., Filzmoser, P., Dimova, R., Neytchev, P.: Robust fitting of mixtures using the trimmed likelihood estimator. Comput. Stat. Data Anal. 52(1), 299–308 (2007)MathSciNet CrossRef MATH
  9.Shah, M., Xiao, Y., Subbanna, N., Francis, S., Arnold, D.L., Collins, D.L., Arbel, T.: Evaluating intensity normalization on MRIs of human brain with multiple sclerosis. Med. Image Anal. 15(2), 267–282 (2011)CrossRef
  10.Shiee, N., Bazin, P.L., Ozturk, A., Reich, D.S., Calabresi, P.A., Pham, D.L.: A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. NeuroImage 49(2), 1524–1535 (2010)CrossRef
  11.Steenwijk, M.D., Pouwels, P.J.W., Daams, M., van Dalen, J.W., Caan, M.W.A., Richard, E., Barkhof, F., Vrenken, H.: Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). NeuroImage Clin. 3, 462–469 (2013)CrossRef
  12.Sweeney, E.M., Shinohara, R.T., Shiee, N., Mateen, F.J., Chudgar, A.A., Cuzzocreo, J.L., Calabresi, P.A., Pham, D.L., Reich, D.S., Crainiceanu, C.M.: OASIS is automated statistical inference for segmentation, with applications to multiple sclerosis lesion segmentation in MRI. NeuroImage Clin. 2, 402–413 (2013)CrossRef
  13.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)CrossRef
  14.Vrenken, H., Jenkinson, M., Horsfield, M.A., Battaglini, M., Schijndel, R.A., Rostrup, E., Geurts, J.J.G., Fisher, E., Zijdenbos, A., Ashburner, J., Miller, D.H., Filippi, M., Fazekas, F., Rovaris, M., Rovira, A., Barkhof, F., de Stefano, N., Group, M.S.: Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis. J. Neurol. 260(10), 2458–2471 (2013)CrossRef
  15.Warfield, S.K., Kaus, M., Jolesz, F.A., Kikinis, R.: Adaptive, template moderated, spatially varying statistical classification. Med. Image Anal. 4(1), 43–55 (2000)CrossRef
  16.Xiao, Y., Shah, M., Francis, S., Arnold, D.L., Arbel, T., Collins, D.L.: Optimal Gaussian mixture models of tissue intensities in brain MRI of patients with multiple-sclerosis. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds.) MLMI 2010. LNCS, vol. 6357, pp. 165–173. Springer, Heidelberg (2010)CrossRef
  
• 作者单位：Tim Jerman (18)
  Alfiia Galimzianova (18)
  Franjo Pernuš (18)
  Boštjan Likar (18)
  Žiga Špiclin (18)
  
  18. Laboratory of Imaging Technologies, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000, Ljubljana, Slovenia
  
• 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

White-matter lesions are associated to several diseases, which can be characterized by neuroimaging biomarkers through lesion segmentation in MR images. We present a novel automated lesion segmentation method consisting of an unsupervised mixture model based extraction of candidate lesion voxels, which are subsequently classified by a random decision forest (RDF) using simple visual features like multi-sequence MR intensities sourced from connected voxel neighborhoods. The candidate lesion extraction prior to RDF training and classification balanced the number of non-lesion and lesion voxels and the number of non-lesion classes versus a lesion class. Thereby, the RDF established highly discriminating decision rules based on such simple visual features, which have the benefit of no computational overhead and easy extraction from the MR images. On MR images of 18 patients with multiple sclerosis the proposed method achieved the median Dice similarity of 0.73, sensitivity of 0.90 and positive predictive value of 0.61, which indicate accurate segmentation of white-matter lesions.