Publications by authors named "V I Chukanov"

The visualization of neuronal activity in vivo is an urgent task in modern neuroscience. It allows neurobiologists to obtain a large amount of information about neuronal network architecture and connections between neurons. The miniscope technique might help to determine changes that occurred in the network due to external stimuli and various conditions: processes of learning, stress, epileptic seizures and neurodegenerative diseases.

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Dendritic spines form most excitatory synaptic inputs in neurons and these spines are altered in many neurodevelopmental and neurodegenerative disorders. Reliable methods to assess and quantify dendritic spines morphology are needed, but most existing methods are subjective and labor intensive. To solve this problem, we developed an open-source software that allows segmentation of dendritic spines from 3D images, extraction of their key morphological features, and their classification and clustering.

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Objective: To describe special algorithm for the semi-autonomous 3-dimensional reconstruction of the pelvicalyceal system based on native computed tomography images of patients with upper urinary tract obstruction.

Materials And Methods: Fifty patients with renal colic fitting to inclusion criteria were enrolled. All patients underwent computed tomography urography to perform 3-dimensional reconstruction of the pelvicalyceal system on the affected size based on excretory phase representing "gold standard" and on native phase, which was performed via Medical Imaging Interaction Toolkit program updated with the described algorithm.

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Purpose: Recent applications of iterative image reconstruction algorithms to multislice helical CT have shown that iterative reconstruction can significantly improve image quality and reduce artifacts. In this paper, the authors introduce a combination of two different algorithms with different convergence properties: ordered subsets separable paraboloidal surrogates (OS-SPS) and iterative coordinate descent (ICD). The first one updates image voxels simultaneously, slightly changing attenuation values iteration by iteration.

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