Meningioma Detection in MR Images Using Convolutional Neural Network and Computer Vision Methods

Yulia D. Agafonova; Andrey V. Gaidel; Evgeniy N. Surovtsev; Aleksandr V. Kapishnikov

doi:10.18287/JBPE20.06.030301

Authors

Yulia D. Agafonova Samara National Research University, Russia
Andrey V. Gaidel Samara National Research University, Russia IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Samara, Russia
Evgeniy N. Surovtsev Samara State Medical University, Russia
Aleksandr V. Kapishnikov LDC MIBS - Tolyatti, Samarskaya obl., Russia

DOI:

https://doi.org/10.18287/JBPE20.06.030301

Keywords:

computer vision, magnetic-resonance imaging, segmentation, convolutional neural network, meningioma

Abstract

The article discusses research efficacy of different architectures of convolutional neural network and methods of computer vision. This paper presents a novel approach to pattern detection of meningioma of the human brain in MR images. MRI images of real patients were made with a help of Samara State Medical University. The result of the research is the automatic procedure of meningioma detection. As a result, post-contrast T1 weighted MRI sequence was the most appropriate for the method based on the baseline statistical segmentation and the diffusion weighted MRI sequence was the most appropriate for the method based on the convolutional neural network.

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