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Convolutional Neural Networks for Automatic Detection of Focal Cortical Dysplasia

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Book cover Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics (Intercognsci 2020)

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Abstract

Focal cortical dysplasia (FCD) is one of the most common epileptogenic lesions associated with cortical development malformations. However, the accurate detection of the FCD relies on the radiologist professionalism, and in many cases, the lesion could be missed. In this work, we solve the problem of automatic identification of FCD on magnetic resonance images (MRI). For this task, we improve recent methods of Deep Learning-based FCD detection and apply it for a dataset of 15 labeled FCD patients. The model results in the successful detection of FCD on 11 out of 15 subjects.

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Notes

  1. 1.

    https://github.com/alievrusik/cnn_fcd_detection.

References

  1. Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)

    Article  Google Scholar 

  2. Sharaev, M., et al.: Mri-based diagnostics of depression con-comitant with epilepsy: in search of the potential biomarkers. In: 201D IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 555–564 (2018)

    Google Scholar 

  3. Tassi, L., Colombo, N., Garbelli, R., Francione, S., Lo Russo, G., Mai, R., Cardinale, F., et al.: Focal cortical dysplasia: neuropathological subtypes. EEG, neuroimaging and surgical outcome. Brain 125(8), 1719–1732 (2002)

    Google Scholar 

  4. Besson, P., Bernasconi, N., Colliot, O., Evans, A., Bernasconi, A.: Surface-based texture and morphological analysis detects subtle cortical dysplasia. In: MICCAI, pp. 645–652 (2008)

    Google Scholar 

  5. Colliot, O., Mansi, T., Bernasconi, N., Naessens, V., Klironomos, D., Bernasconi, A.: Segmentation of focal cortical dysplasia lesions on mri using level set evolution. Neuroimage 32(4), 1621–1630 (2006)

    Article  Google Scholar 

  6. Pominova, M., Artemov, A., Sharaev, M., Kondrateva, E., Burnaev, A., Bern-stein, E.: Voxelwise 3d convolutional and recurrent neural networks for epilepsy and depression diagnostics from structural and functional mri data. In: Proceedings of IEEE International Conference on Data Mining Workshops (ICDMW), pp. 299–307 (2018)

    Google Scholar 

  7. Dev, K.B., Jogi, P.S., Niyas, S., Vinayagamani, S., Kesavadas, C., Rajan, J.: Automatic detection and localization of focal cortical dysplasia lesions in MRI using fully convolutional neural network. Biomed. Signal Process. Control 52, 218–225 (2019)

    Google Scholar 

  8. Wang, H., Ahmed, S.N., Mandal, M.: Automated detection of focal cortical dys- plasia using a deep convolutional neural network. Comput. Med. Imaging Graph. 79, 101662 (2020)

    Article  Google Scholar 

  9. Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)

    Google Scholar 

  10. Poldrack, R.A., Barch, D.M., Mitchell, J., Wager, T., Wagner, A.D., Devlin, J.T., Cumba, C., Koyejo, O., Milham, M.: Toward open sharing of task-based fmri data: the openfMRI project. Frontiers neuroinformatics 7, 12 (2013)

    Article  Google Scholar 

  11. Pavlov, S., Artemov, A., Sharaev, M., Bernstein, A.E.: Weakly supervised fine tuning approach for brain tumor segmentation problem. In: Proceedings of ICMLA, pp. 1600–1605 (2019)

    Google Scholar 

  12. Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., et al.: A probabilistic atlas and reference system for the human brain: Inter national consortium for brain mapping (icbm). Philosophical Trans. of the Royal Society of London. Ser. B: Biol. Sci. 356(1412), 1293–1322 (2001)

    Google Scholar 

  13. Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: Fsl. Neuroimage 60(2), 782–790 (2012)

    Article  Google Scholar 

  14. Reuter, M., Schmansky, N.J., et al.: Within-subject template estimation for unbi-ased longitudinal image analysis. Neuroimage 61(4), 1402–1418 (2012)

    Article  Google Scholar 

  15. Pérez-Garcia, F., Sparks, R., Ourselin, S.: TorchIO: a python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. arXiv: 2003.04696 [cs, cess, stat] (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Skolkovo Institute of Science and Technology, Moscow, Russia

    Ruslan Aliev, Ekaterina Kondrateva, Maxim Sharaev, Alexander Bernstein & Evgeny Burnaev

  2. National Medical, Surgical Center named after N.I. Pirogov, Moscow, Russia

    Oleg Bronov, Alexey Marinets & Sergey Subbotin

Authors
  1. Ruslan Aliev
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  2. Ekaterina Kondrateva
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  3. Maxim Sharaev
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  4. Oleg Bronov
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  5. Alexey Marinets
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  6. Sergey Subbotin
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  7. Alexander Bernstein
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  8. Evgeny Burnaev
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Corresponding author

Correspondence to Ruslan Aliev .

Editor information

Editors and Affiliations

  1. National Research Center "Kurchatov Institute", Moscow, Russia

    Boris M. Velichkovsky

  2. Institute of Higher Nervous Activity RAS, Moscow, Russia

    Pavel M. Balaban

  3. Institute for Advanced Brain Studies, Lomonosov Moscow State University, Moscow, Russia

    Vadim L. Ushakov

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Cite this paper

Aliev, R. et al. (2021). Convolutional Neural Networks for Automatic Detection of Focal Cortical Dysplasia. In: Velichkovsky, B.M., Balaban, P.M., Ushakov, V.L. (eds) Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics. Intercognsci 2020. Advances in Intelligent Systems and Computing, vol 1358. Springer, Cham. https://doi.org/10.1007/978-3-030-71637-0_67

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