Andrei Yamaev Viktorovich
@smartengines.com
researcher
Smart Engines
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Radiation
Scopus Publications
Scopus Publications
Aleksandr Smolin, Marina Chukalina, Inna Bukreeva, Olga Junemann, Alessia Cedola, Michela Fratini, Sergei Saveliev, and Andrey Yamaev
SPIE
A. S. Ingacheva, M. I. Gilmanov, A. V. Yamaev, A. V. Buzmakov, D. D. Kazimirov, I. A. Kunina, Zh. V. Soldatova, M. V. Chukalina, and V. V. Arlazarov
Pleiades Publishing Ltd
Aleksandr Smolin, Andrei Yamaev, Anastasia Ingacheva, Tatyana Shevtsova, Dmitriy Polevoy, Marina Chukalina, Dmitry Nikolaev, and Vladimir Arlazarov
MDPI AG
In computed tomography, state-of-the-art reconstruction is based on neural network (NN) algorithms. However, NN reconstruction algorithms can be not robust to small noise-like perturbations in the input signal. A not robust NN algorithm can produce inaccurate reconstruction with plausible artifacts that cannot be detected. Hence, the robustness of NN algorithms should be investigated and evaluated. There have been several attempts to construct the numerical metrics of the NN reconstruction algorithms’ robustness. However, these metrics estimate only the probability of the easily distinguishable artifacts occurring in the reconstruction. However, these methods measure only the probability of appearance of easily distinguishable artifacts on the reconstruction, which cannot lead to misdiagnosis in clinical applications. In this work, we propose a new method for numerical estimation of the robustness of the NN reconstruction algorithms. This method is based on the probability evaluation for NN to form such selected additional structures during reconstruction which may lead to an incorrect diagnosis. The method outputs a numerical score value from 0 to 1 that can be used when benchmarking the robustness of different reconstruction algorithms. We employed the proposed method to perform a comparative study of seven reconstruction algorithms, including five NN-based and two classical. The ResUNet network had the best robustness score (0.65) among the investigated NN algorithms, but its robustness score is still lower than that of the classical algorithm SIRT (0.989). The investigated NN models demonstrated a wide range of robustness scores (0.38–0.65). Thus, in this work, robustness of 7 reconstruction algorithms was measured using the new proposed score and it was shown that some of the neural algorithms are not robust.
Alexandr Meshkov, Anvar Khafizov, Alexey Buzmakov, Inna Bukreeva, Olga Junemann, Michela Fratini, Alessia Cedola, Marina Chukalina, Andrei Yamaev, Giuseppe Gigli,et al.
MDPI AG
The human olfactory bulb (OB) has a laminar structure. The segregation of cell populations in the OB image poses a significant challenge because of indistinct boundaries of the layers. Standard 3D visualization tools usually have a low resolution and cannot provide the high accuracy required for morphometric analysis. X-ray phase contrast tomography (XPCT) offers sufficient resolution and contrast to identify single cells in large volumes of the brain. The numerous microanatomical structures detectable in XPCT image of the OB, however, greatly complicate the manual delineation of OB neuronal cell layers. To address the challenging problem of fully automated segmentation of XPCT images of human OB morphological layers, we propose a new pipeline for tomographic data processing. Convolutional neural networks (CNN) were used to segment XPCT image of native unstained human OB. Virtual segmentation of the whole OB and an accurate delineation of each layer in a healthy non-demented OB is mandatory as the first step for assessing OB morphological changes in smell impairment research. In this framework, we proposed an effective tool that could help to shed light on OB layer-specific degeneration in patients with olfactory disorder.
A.V. Yamaev, , M.V. Chukalina, D.P. Nikolaev, L.G. Kochiev, A.I. Chulichkov, , , , ,et al.
Samara National Research University
The computed tomography allows to reconstruct the inner morphological structure of an object without physical destructing. The accuracy of digital image reconstruction directly depends on the measurement conditions of tomographic projections, in particular, on the number of recorded projections. In medicine, to reduce the dose of the patient load there try to reduce the number of measured projections. However, in a few-view computed tomography, when we have a small number of projections, using standard reconstruction algorithms leads to the reconstructed images degradation. The main feature of our approach for few-view tomography is that algebraic reconstruction is being finalized by a neural network with keeping measured projection data because the additive result is in zero space of the forward projection operator. The final reconstruction presents the sum of the additive calculated with the neural network and the algebraic reconstruction. First is an element of zero space of the forward projection operator. The second is an element of orthogonal addition to the zero space. Last is the result of applying the algebraic reconstruction method to a few-angle sinogram. The dependency model between elements of zero space of forward projection operator and algebraic reconstruction is built with neural networks. It demonstrated that realization of the suggested approach allows achieving better reconstruction accuracy and better computation time than state-of-the-art approaches on test data from the Low Dose CT Challenge dataset without increasing reprojection error.
A. V. Yamaev, M. V. Chukalina, D. P. Nikolaev, A. V. Sheshkus, and A. I. Chulichkov
Pleiades Publishing Ltd
Andrei Yamaev, Marina Chukalina, Dmitry Nikolaev, Alexander Sheshkus, and Alexey Chulichkov
SPIE
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.