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

Yulia D. Agafonova (Login required)
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

Paper #3382 received 26 Aug 2020; revised manuscript received 18 Sep 2020; accepted for publication 20 Sep 2020; published online 30 Sep 2020.

DOI: 10.18287/JBPE20.06.030301


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.


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

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