Surface-Wise Texture Patch Analysis of Combined MRI and PET to Detect MRI-Negative Focal Cortical Dysplasia

  • Hosung KimEmail author
  • Yee-Leng Tan
  • Seunghyun Lee
  • Anthony James Barkovich
  • Duan Xu
  • Robert Knowlton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


Focal cortical dysplasia (FCD) is intrinsically epileptogenic, and is a well-recognized cause of refractory epilepsy. Surgical resection can lead to seizure-freedom, and quantitative analyses of MRI have been an excellent presurgical evaluation tool. Yet many of the FCDs are small or “MRI-negative” at the visual evaluation, for which positron emission tomography (PET) may help identify an MRI-negative FCD. We proposed a pipeline that optimized cortical surface sampling of combined MRI and PET features and detection of subtle or visually negative FCDs. To this end, we extracted individual cortical surfaces and designed an adapted between-image modalities registration that conveyed the surface mesh into each image’s native space. To further improve detection accuracy, we integrated a framework of the patch library construction and label fusion that have been widely used for the brain structural segmentation into a surface-based classification. Our study demonstrated superior sensitivity in FCD detection using combined feature sampling of both MRI and PET, compared to MRI alone (93 vs 86%) while identifying no false positives (FP) in controls. Our classifier using the new patch analysis further outperformed a recently developed surface-based approach in terms of sensitivity (93 vs 64%), and FP rate (0 vs 0.1%). No FP vertices were found in controls: our classifier yet identified extralesional clusters in FCD patients. Patients with a higher prevalence of extralesional vertices had a lower chance of positive surgical outcome compared to those with a lower prevalence (67% vs 93%; p < 0.05).

Our study showed that the advance in techniques for multimodal feature sampling is the key to improve lesion detection and understand the pattern of brain abnormalities in FCD.


Focal cortical dysplasia MRI negative PET hypometabolism Surface-based classifier Patch-based segmentation 


  1. 1.
    Blumcke, I., Thom, M., Aronica, E., Armstrong, D.D., et al.: The clinicopathologic spectrum of focal cortical dysplasias. Epilepsia 52(1), 158–174 (2011)CrossRefGoogle Scholar
  2. 2.
    Semah, F., Picot, M.C., Adam, C., Broglin, D., et al.: Is the underlying cause of epilepsy a major prognostic factor for recurrence? Neurology 51(5), 1256–1262 (1998)CrossRefGoogle Scholar
  3. 3.
    Antel, S.B., Bernasconi, A., Bernasconi, N., Collins, D.L., et al.: Computational models of MRI characteristics of focal cortical dysplasia improve lesion detection. Neuroimage 17(4), 1755–1760 (2002)CrossRefGoogle Scholar
  4. 4.
    Salamon, N., Kung, J., Shaw, S.J., et al.: FDG-PET/MRI coregistration improves detection of cortical dysplasia in patients with epilepsy. Neurology 71(20), 1594–1601 (2008)CrossRefGoogle Scholar
  5. 5.
    Mellerio, C., Labeyrie, M.A., Chassoux, F., Daumas-Duport, C., et al.: Optimizing MR imaging detection of type 2 focal cortical dysplasia: best criteria for clinical practice. AJNR Am. J. Neuroradiol. 33(10), 1932–1938 (2012)CrossRefGoogle Scholar
  6. 6.
    Ahmed, B., Brodley, C.E., Blackmon, K.E., Kuzniecky, R., et al.: Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia. Epilepsy Behav. 48, 21–28 (2015)CrossRefGoogle Scholar
  7. 7.
    Besson, P., Bernasconi, N., Colliot, O., Evans, A., Bernasconi, A.: Surface-based texture and morphological analysis detects subtle cortical dysplasia. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 645–652. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-85988-8_77CrossRefGoogle Scholar
  8. 8.
    Hong, S.J., Kim, H., Schrader, D., Bernasconi, N., et al.: Automated detection of cortical dysplasia type II in MRI-negative epilepsy. Neurology 83(1), 48–55 (2014)CrossRefGoogle Scholar
  9. 9.
    Coupe, P., Manjon, J.V., Fonov, V., Pruessner, J., et al.: Patch-based segmentation using expert priors. Neuroimage 54(2), 940–954 (2011)CrossRefGoogle Scholar
  10. 10.
    Wang, L., Shi, F., Li, G., Gao, Y.Z., et al.: Segmentation of neonatal brain MR images using patch-driven level sets. Neuroimage 84, 141–158 (2014)CrossRefGoogle Scholar
  11. 11.
    Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., et al.: Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54(1), 313–327 (2011)CrossRefGoogle Scholar
  12. 12.
    Kim, J.S., Singh, V., Lee, J.K., Lerch, J., et al.: Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage 27(1), 210–221 (2005)CrossRefGoogle Scholar
  13. 13.
    Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., et al.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115–126 (2006)CrossRefGoogle Scholar
  14. 14.
    Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de-Solorzano, C.: Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans. Med. Imaging 28(8), 1266–1277 (2009)CrossRefGoogle Scholar
  15. 15.
    Lotjonen, J.M., Wolz, R., Koikkalainen, J., Thurfjell, L., et al.: Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage 49(3), 2352–2365 (2010)CrossRefGoogle Scholar
  16. 16.
    Colombo, N., Tassi, L., Deleo, F., Citterio, A., et al.: Focal cortical dysplasia type IIa and IIb. Neuroradiology 54(10), 1065–1077 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hosung Kim
    • 1
    • 2
    • 3
    Email author
  • Yee-Leng Tan
    • 1
    • 5
  • Seunghyun Lee
    • 4
  • Anthony James Barkovich
    • 2
  • Duan Xu
    • 2
  • Robert Knowlton
    • 1
  1. 1.Department of NeurologyUniversity of California, San FranciscoSan FranciscoUSA
  2. 2.Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoUSA
  3. 3.USC Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesUSA
  4. 4.Department of RadiologySeoul National University HospitalSeoulRepublic of Korea
  5. 5.Department of NeurologyNational Neuroscience InstituteSingaporeSingapore

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