The Skychain Solution

The Skychain AI for brain glioma recognition available in Skychain Alpha detects gliomas by analyzing MRI images.

Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body in both health and disease. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body.

For more complete and accurate segmentation, the Skychain neural network uses three-dimensional images of the brain MRI of the following four types:

MRI-sequence FLAIR (Fluid attenuation inversion recovery);

T1-weighted sequence;

T1c-weighted sequence with increased contrast (Contrast Enhanced);

T2-weighted sequence.

The U-Net is used as the AI architecture. It is built upon the Fully Convolutional Network and modified in a way that it yields better segmentation in medical imaging.

The neural network identifies the following areas of glioma:

Necrotic tumor core (Mark 1, red color );

); Peritumoral edema (Mark 2, green color );

); Enhancing core (Mark 3, purple color).

The result of segmentation is presented to a user as a three-dimensional brain model.

Maria Piliugina, who developed this neural network, reports that the current accuracy rate is about 87%. For estimating this she used dice coefficient which characterizes the similarity between two samples(true segmentation and the ANN segmentation).

Dice coefficient formula

Further improvement of network characteristics will be facilitated by the expansion of the base of training images and segmentations.

Watch the interview with Skychain developer Maria Piliugina on the AI for brain glioma recognition:

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Iva Chernysheva, Marketing Manager