Mr. Andrei Yamaev Profile

Andrei Yamaev

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Proceedings Article | 3 April 2024 Paper

Segmentation of human olfactory bulb glomeruli on its phase-contrast tomographic images with neural networks

Aleksandr Smolin, Marina Chukalina, Inna Bukreeva, Olga Junemann, Alessia Cedola, Michela Fratini, Sergei Saveliev, Andrey Yamaev

Proceedings Volume 13072, 1307209 (2024) https://doi.org/10.1117/12.3023398

KEYWORDS: Education and training, Data modeling, Interpolation

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The human olfactory bulb (OB), an important part of the brain responsible for the sense of smell, is a complex structure composed of multiple layers and cell types. Studying the OB morphological structure is essential for understanding the decline in olfactory function related to aging, neurodegenerative disorders, and other pathologies. Traditional microscopy methods in which slices are stained with solutions to contrast individual elements of the morphological structure are destructive. Non-destructive high-resolution technique is the X-ray phase-contrast tomography. However, manual segmentation of the reconstructed images are time-consuming due to large amount of data and prone to errors. U-Net-based model to optimize the segmentation of OB morphological structures, focusing specifically on glomeruli, in tomographic images of the human OB is proposed. The strategy to address overfitting and enhance the model's accuracy is described. This method addresses the challenges posed by complex limited data containing abundant details, similar grayscale levels between soft tissues, and blurry image details. Additionally, it successfully overcomes the limitations of a small dataset containing images with extremely dense point clouds, preventing the models from overfitting.

Proceedings Article | 4 January 2021 Paper

Lightweight denoising filtering neural network for FBP algorithm

Andrei Yamaev, Marina Chukalina, Dmitry Nikolaev, Alexander Sheshkus, Alexey Chulichkov

Proceedings Volume 11605, 116050L (2021) https://doi.org/10.1117/12.2587185

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In that paper, we a suggest lightweight filtering neural network, which implements the filtering stage in the Filtered Back-Projection algorithm (FBP), but good reconstruction results are achieved not only in ideal data but also in noisy data, which a usual FBP algorithm cannot achieve. Thus, our neural network is not an only variation of Ramp filter, which is usually used then FBP algorithm, but also a denoising filter. The neural network architecture was inspired with the idea of the possibility of the Ramp filtering operation’s approximation with sufficient accuracy. The efficiency of our network was shown on the synthetic data, which imitate tomographic projections collected with low exposition. In the generation of synthetic data, we have taken into account the quantum nature of X-ray radiation, exposition time of one frame, and non-linear detector response. The FBP reconstruction time with our neural network was 13 times faster than the time of reconstruction neural network from Learned Primal-Dual Reconstruction, and our reconstruction quality 0.906 by SSIM metric, which is enough to identify most significant objects.

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