Software for Full-Color 3D Reconstruction of the Biological Tissues Internal Structure

Хоперсков, А. В.; Kovalev, M.E.; Astakhov, A.S.; Новочадов, В. В.; Terpilovskiy, A.A.; Tiras, Kharlampiy; Маланин, Д А

doi:10.1007/978-3-319-69182-4_1

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…An equally important factor is the internal structure, which is determined by the location of various components of connective, muscle, and epithelial tissues, as well as their size and geometric shape (Figure 5). The construction of a geometric model of the internal structure of biological tissues can be based on 3D reconstruction using data from a repository of magnetic resonance imaging images or high-precision layer-by-layer grinding of frozen biological tissues [71], which is full-color and provides resolution to 5 µm, in contrast to MRI data with a resolution of approximately 0.5 cm. An important disadvantage of these approaches is the small number of distinct instances with different internal structures.…”

Section: Methods For 3d Reconstruction Of Multicomponent Tissuementioning

confidence: 99%

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

Хоперсков

Polyakov

2022

Algorithms

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This work includes a brief overview of the applications of the powerful and easy-to-perform method of microwave radiometry (MWR) for the diagnosis of various diseases. The main goal of this paper is to develop a method for diagnosing breast oncology based on machine learning algorithms using thermometric data, both real medical measurements and simulation results of MWR examinations. The dataset includes distributions of deep and skin temperatures calculated in numerical models of the dynamics of thermal and radiation fields inside biological tissue. The constructed combined dataset allows us to explore the limits of applicability of the MWR method for detecting weak tumors. We use convolutional neural networks and classic machine learning algorithms (k-nearest neighbors, naive Bayes classifier, support vector machine) to classify data. The construction of Kohonen self-organizing maps to explore the structure of our combined dataset demonstrated differences between the temperatures of patients with positive and negative diagnoses. Our analysis shows that the MWR can detect tumors with a radius of up to 0.5 cm if they are at the stage of rapid growth, when the tumor volume doubling occurs in approximately 100 days or less. The use of convolutional neural networks for MWR provides both high sensitivity (sens=0.86) and specificity (spec=0.82), which is an advantage over other methods for diagnosing breast cancer. A new modified scheme for medical measurements of IR temperature and brightness temperature is proposed for a larger number of points in the breast compared to the classical scheme. This approach can increase the effectiveness and sensitivity of diagnostics by several percent.

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Section: Methods For 3d Reconstruction Of Multicomponent Tissuementioning

confidence: 99%

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

Хоперсков

Polyakov

2022

Algorithms

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“…Universal computer network technologies do not allow combining different types of medical equipment [21][22][23][24][25]42]. Therefore, its manufacturers were forced to develop their communication interfaces.…”

Section: Introductionmentioning

confidence: 99%

Prospects for Creating 3D Models of Internal Organs Based on Computer and Magnetic Resonance Imaging Images in Emergency Surgery and Resuscitation

Mezhidov¹,

Belyaeva²,

S-M.Bimarzaev³

et al. 2021

Pharmacophore

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The present article gives an analytical overview of the current state of processing problems and visualization of magnetic resonance imaging (MRI). It is presented the expediency of combining of instrumental means, presented in various open-source programs and scientific publications in recent years, into a single program-tool complex solving in full problems of pre-processing and filtering data, directed precision thematic segmentation and analysis of images, forming the feature space, recognition of the situation, 3D modeling, and visualization. The complex is designed for expert decision support of doctors-researchers in the process of carrying out scientific experiments. Support is based on the application of new methods of brain area texture analysis, effective metrics, and advanced means of cognitive computer graphics. An important feature of the complex must be the interactive operating mode of the doctor-researcher with MRI data, which can be provided by applying high-performance computing with software support of hardware accelerators.

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Improving the Quality of Dental Services Based on Metal Additive Technologies: Unified Digital Workflow of Treatment

Radchenko,

Khoperskov

2023

Communications in Computer and Information Science

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Software for Full-Color 3D Reconstruction of the Biological Tissues Internal Structure

Cited by 5 publications

References 21 publications

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

Prospects for Creating 3D Models of Internal Organs Based on Computer and Magnetic Resonance Imaging Images in Emergency Surgery and Resuscitation

Improving the Quality of Dental Services Based on Metal Additive Technologies: Unified Digital Workflow of Treatment

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