Convolutional neural networks for automatic detection of Focal Cortical Dysplasia

Ruslan Aliev; Ekaterina Kondrateva; Maxim Sharaev; Oleg Bronov; Alexey Marinets; Sergey Subbotin; Alexander Bernstein; Evgeny Burnaev

Convolutional neural networks for automatic detection of Focal Cortical Dysplasia

Image and Video Processing 2020-10-26 v1 Computer Vision and Pattern Recognition

Authors: Ruslan Aliev , Ekaterina Kondrateva , Maxim Sharaev , Oleg Bronov , Alexey Marinets , Sergey Subbotin , Alexander Bernstein , Evgeny Burnaev

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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.

Keywords

focal cortical dysplasia automatic detection convolutional neural networks deep learning magnetic resonance images epileptogenic lesions

Cite

@article{arxiv.2010.10373,
  title  = {Convolutional neural networks for automatic detection of Focal Cortical Dysplasia},
  author = {Ruslan Aliev and Ekaterina Kondrateva and Maxim Sharaev and Oleg Bronov and Alexey Marinets and Sergey Subbotin and Alexander Bernstein and Evgeny Burnaev},
  journal= {arXiv preprint arXiv:2010.10373},
  year   = {2020}
}

Comments

MRI, Deep learning, CNN, computer vision, medical detection, epilepsy, FCD, focal cortical dysplasia

Convolutional neural networks for automatic detection of Focal Cortical Dysplasia

Abstract

Keywords

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