Gleb Vdovin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Gleb Vdovin has a total of 25 co-authors. Here are the top co-authors by number of shared publications:
- Oleg Soloviev
Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
4 shared publications - Jan Carel Diehl
Delft University of Technology
3 shared publications - Temitope Agbana
Delft University of Technology, the Netherlands
3 shared publications - Angela van Diepen
Leiden University Center for Infectious Diseases – Research (LUCID-R), Leiden University Medical Center
2 shared publications - Brice Meulah
Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, the Netherlands
2 shared publications - Hai Gong
Huzhou Institute of Zhejiang University, Zhejiang University, Huzhou, China
2 shared publications
Latest Publications
- Open Access
- 1 July 2023
- Published by SPIE-Intl Soc Optical Eng in Journal of Medical Imaging
- Vol. 10 (04) , 044005-17
Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings
Schistosomiasis is endemic in 76 countries worldwide with people infected and an estimated 779 million people at risk of infection.1 Schistosomiasis is caused by blood flukes of the genus Schistosoma (S); both S. mansoni (intestinal schistosomiasis) and S. haematobium (urogenital schistosomiasis) are endemic in Africa.2 Schistosomiasis presents a substantial public health and economic burden as it is a disease of poverty. In the drive to attain the World Health Organization (WHO) control and elimination targets, diagnosis for adequate monitoring of interventions and surveillance is critical.2,3 Recently, the WHO published the diagnostic target product profiles (TPP) for monitoring, evaluation, and surveillance of schistosomiasis control programs,4 which identifies development of diagnostic tests for S. haematobium detection as a high-risk requirement due to lack of its availability. The TPP suggests a semi-quantitative analysis, capable of providing some degree of information regarding intensity of infection, as ideal for a diagnostic test for schistosomiasis to support monitoring and evaluation.4 Currently, microscopy is the WHO reference standard for the diagnosis of schistosomiasis in resource-limited settings. For the detection of S. haematobium infection, urine samples, after filtration, sedimentation, or centrifugation, are microscopically examined for the presence of eggs.3 This method is operator dependent, costly, laborious, and time-consuming. Furthermore, it requires expertise, which means microscopy skills need to be gained and maintained, which can be an economic challenge, particularly in remote rural communities.3 There is also the risk of visual health complications among microscopists resulting from excessive workload due to the low ratio of trained microscopists to samples for analysis in endemic regions.5 Hence, a field adaptable, rapid, and easy-to-use automated diagnosis is relevant for the prompt detection of cases, which will facilitate mapping and monitoring of interventions.4 Recent advances in opto-mechanics and opto-electronics have rapidly transformed the field of biomedical optics. Optical imaging technologies, such as conventional light microscopes, are being redesigned to integrate and miniaturize portable light microscopes for use at the point of care.6 – 9 Although these technologies are readily available in high-income countries, unfortunately, nearly all schistosomiasis cases are seen in low-resource regions of low-income countries, significantly justifying the need for cost-effective and easy-to-use smart diagnostic technologies. In this work, we address these challenges by first increasing the size of the S. haematobium (SH) dataset in our previous work10 from 5198 to 12,051 images of clinical samples.11 We carry out detection and counting of S. haematobium eggs present in each image by proposing a two-stage framework consisting of a DeepLabv3 with MobilenetV3 backbone deep convolutional neural network12 trained on the SH dataset using a transfer learning approach. The second stage of our proposed framework is a refined segmentation and egg counting procedure, which adapts the region-based fitting of overlapping ellipses13 to efficiently separate the boundaries of overlapping eggs in the image. Finally, the detected isolated eggs are screened for the presence of an egg, which meets the defined boundary condition before the sample can be determined as positive/negative diagnosis. We further demonstrate the robustness and applicability of the proposed framework for field diagnoses of urogenital schistosomiasis by implementing our framework on an Edge AI system (Raspberry Pi + Coral USB accelerator) and testing 65 clinical urine samples obtained in a field settings in Nigeria. The main contributions of this work can be summarized as follows.- 1 July 2023
- Published by SPIE-Intl Soc Optical Eng in Journal of Medical Imaging
- Vol. 10 (04) , 044005-17
Open AccessTwo-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings
Schistosomiasis is endemic in 76 countries worldwide with people infected and an estimated 779 million people at risk of infection.1 Schistosomiasis is caused by blood flukes of the genus Schistosoma (S); both S. mansoni (intestinal schistosomiasis) and S. haematobium (urogenital schistosomiasis) are endemic in Africa.2 Schistosomiasis presents a substantial public health and economic burden as it is a disease of poverty. In the drive to attain the World Health Organization (WHO) control and elimination targets, diagnosis for adequate monitoring of interventions and surveillance is critical.2,3 Recently, the WHO published the diagnostic target product profiles (TPP) for monitoring, evaluation, and surveillance of schistosomiasis control programs,4 which identifies development of diagnostic tests for S. haematobium detection as a high-risk requirement due to lack of its availability. The TPP suggests a semi-quantitative analysis, capable of providing some degree of information regarding intensity of infection, as ideal for a diagnostic test for schistosomiasis to support monitoring and evaluation.4 Currently, microscopy is the WHO reference standard for the diagnosis of schistosomiasis in resource-limited settings. For the detection of S. haematobium infection, urine samples, after filtration, sedimentation, or centrifugation, are microscopically examined for the presence of eggs.3 This method is operator dependent, costly, laborious, and time-consuming. Furthermore, it requires expertise, which means microscopy skills need to be gained and maintained, which can be an economic challenge, particularly in remote rural communities.3 There is also the risk of visual health complications among microscopists resulting from excessive workload due to the low ratio of trained microscopists to samples for analysis in endemic regions.5 Hence, a field adaptable, rapid, and easy-to-use automated diagnosis is relevant for the prompt detection of cases, which will facilitate mapping and monitoring of interventions.4 Recent advances in opto-mechanics and opto-electronics have rapidly transformed the field of biomedical optics. Optical imaging technologies, such as conventional light microscopes, are being redesigned to integrate and miniaturize portable light microscopes for use at the point of care.6 – 9 Although these technologies are readily available in high-income countries, unfortunately, nearly all schistosomiasis cases are seen in low-resource regions of low-income countries, significantly justifying the need for cost-effective and easy-to-use smart diagnostic technologies. In this work, we address these challenges by first increasing the size of the S. haematobium (SH) dataset in our previous work10 from 5198 to 12,051 images of clinical samples.11 We carry out detection and counting of S. haematobium eggs present in each image by proposing a two-stage framework consisting of a DeepLabv3 with MobilenetV3 backbone deep convolutional neural network12 trained on the SH dataset using a transfer learning approach. The second stage of our proposed framework is a refined segmentation and egg counting procedure, which adapts the region-based fitting of overlapping ellipses13 to efficiently separate the boundaries of overlapping eggs in the image. Finally, the detected isolated eggs are screened for the presence of an egg, which meets the defined boundary condition before the sample can be determined as positive/negative diagnosis. We further demonstrate the robustness and applicability of the proposed framework for field diagnoses of urogenital schistosomiasis by implementing our framework on an Edge AI system (Raspberry Pi + Coral USB accelerator) and testing 65 clinical urine samples obtained in a field settings in Nigeria. The main contributions of this work can be summarized as follows. - Open Access
- 19 April 2022
- Published by MDPI AG in Micromachines
- Vol. 13 (5) , 643
Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings
For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.
- 19 April 2022
- Published by MDPI AG in Micromachines
- Vol. 13 (5) , 643
Open AccessSchistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings
For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.
- Open Access
- 9 January 2021
- Published by SPIE-Intl Soc Optical Eng in Journal of Biomedical Optics
- Vol. 26 (01) , 016502-16
Large volume holographic imaging for biological sample analysis
Particle field holography (PFH) is an important domain developed after the invention of holography by Gabor.1 Originating in the early sixties,2,3 it proved a versatile technique to determine the size and distribution of moving or stationary particles in air or in a liquid without significant disturbance of the sample volume. The introduction of solid-state imagers, as replacements of the photographic films, and the recent development of computational methods to numerically reconstruct the object from the recorded hologram, has resulted in the rapid development and application of PFH in diverse fields.4 – 7
- 9 January 2021
- Published by SPIE-Intl Soc Optical Eng in Journal of Biomedical Optics
- Vol. 26 (01) , 016502-16
Open AccessLarge volume holographic imaging for biological sample analysis
Particle field holography (PFH) is an important domain developed after the invention of holography by Gabor.1 Originating in the early sixties,2,3 it proved a versatile technique to determine the size and distribution of moving or stationary particles in air or in a liquid without significant disturbance of the sample volume. The introduction of solid-state imagers, as replacements of the photographic films, and the recent development of computational methods to numerically reconstruct the object from the recorded hologram, has resulted in the rapid development and application of PFH in diverse fields.4 – 7
- Open Access
- 1 December 2019
- Published by ITMO University in Scientific and Technical Journal of Information Technologies, Mechanics and Optics
- Vol. 124 (6) , 959
Сomparison of on-line and off-line Fried parameter estimation methods
We address the problem of high-accuracy estimation of the Fried parameter r0 by comparing two approaches based on estimation of statistical properties of intensity-based wavefront measurements with parameter-fitting to the theoretically predicted values. In the first approach, the phase of the aberration-degraded field is restored from the measurements to obtain the statistical estimate for the structure function. Due to the iterative nature of the most phase retrieval methods, this approach requires significant computational time and thus cannot provide results in real time. In the second approach, the structure function of the sub-aperture wavefront slopes is directly calculated and related to the turbulence parameters in real time. We describe the equations used to obtain the estimate of the Fried parameter by both methods and check their accuracy with numerical simulations.
- 1 December 2019
- Published by ITMO University in Scientific and Technical Journal of Information Technologies, Mechanics and Optics
- Vol. 124 (6) , 959
Open AccessСomparison of on-line and off-line Fried parameter estimation methods
We address the problem of high-accuracy estimation of the Fried parameter r0 by comparing two approaches based on estimation of statistical properties of intensity-based wavefront measurements with parameter-fitting to the theoretically predicted values. In the first approach, the phase of the aberration-degraded field is restored from the measurements to obtain the statistical estimate for the structure function. Due to the iterative nature of the most phase retrieval methods, this approach requires significant computational time and thus cannot provide results in real time. In the second approach, the structure function of the sub-aperture wavefront slopes is directly calculated and related to the turbulence parameters in real time. We describe the equations used to obtain the estimate of the Fried parameter by both methods and check their accuracy with numerical simulations.
- Open Access
- 20 December 2018
- Published by MDPI AG in Methods and Protocols
- Vol. 2 (1) , 2
Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm
The use of spatial light modulators to project computer generated holograms is a common strategy for optogenetic stimulation of multiple structures of interest within a three-dimensional volume. A common requirement when addressing multiple targets sparsely distributed in three dimensions is the generation of a points cloud, focusing excitation light in multiple diffraction-limited locations throughout the sample. Calculation of this type of holograms is most commonly performed with either the high-speed, low-performance random superposition algorithm, or the low-speed, high performance Gerchberg–Saxton algorithm. This paper presents a variation of the Gerchberg–Saxton algorithm that, by only performing iterations on a subset of the data, according to compressive sensing principles, is rendered significantly faster while maintaining high quality outputs. The algorithm is presented in high-efficiency and high-uniformity variants. All source code for the method implementation is available as Supplementary Materials and as open-source software. The method was tested computationally against existing algorithms, and the results were confirmed experimentally on a custom setup for in-vivo multiphoton optogenetics. The results clearly show that the proposed method can achieve computational speed performances close to the random superposition algorithm, while retaining the high performance of the Gerchberg–Saxton algorithm, with a minimal hologram quality loss.
- 20 December 2018
- Published by MDPI AG in Methods and Protocols
- Vol. 2 (1) , 2
Open AccessFast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm
The use of spatial light modulators to project computer generated holograms is a common strategy for optogenetic stimulation of multiple structures of interest within a three-dimensional volume. A common requirement when addressing multiple targets sparsely distributed in three dimensions is the generation of a points cloud, focusing excitation light in multiple diffraction-limited locations throughout the sample. Calculation of this type of holograms is most commonly performed with either the high-speed, low-performance random superposition algorithm, or the low-speed, high performance Gerchberg–Saxton algorithm. This paper presents a variation of the Gerchberg–Saxton algorithm that, by only performing iterations on a subset of the data, according to compressive sensing principles, is rendered significantly faster while maintaining high quality outputs. The algorithm is presented in high-efficiency and high-uniformity variants. All source code for the method implementation is available as Supplementary Materials and as open-source software. The method was tested computationally against existing algorithms, and the results were confirmed experimentally on a custom setup for in-vivo multiphoton optogenetics. The results clearly show that the proposed method can achieve computational speed performances close to the random superposition algorithm, while retaining the high performance of the Gerchberg–Saxton algorithm, with a minimal hologram quality loss.
- 8 October 2015
- Published by IOP Publishing in Journal of Optics
- Vol. 17 (12) , 122001
Lensless coherent imaging by sampling of the optical field with digital micromirror device
We have experimentally demonstrated a lensless coherent microscope based on direct registration of the complex optical field by sampling the pupil with a sequence of two-point interferometers formed by a digital micromirror device. Complete registration of the complex amplitude in the pupil of the imaging system, without any reference beam, provides a convenient link between the experimental and computational optics. Unlike other approaches to digital holography, our method does not require any external reference beam, resulting in a simple and robust registration setup. Computer analysis of the experimentally registered field allows for focusing the image in the whole range from zero to infinity, and for virtual correction of the aberrations present in the real optical system, by applying the adaptive wavefront corrections to its virtual model.
- 8 October 2015
- Published by IOP Publishing in Journal of Optics
- Vol. 17 (12) , 122001
Lensless coherent imaging by sampling of the optical field with digital micromirror device
We have experimentally demonstrated a lensless coherent microscope based on direct registration of the complex optical field by sampling the pupil with a sequence of two-point interferometers formed by a digital micromirror device. Complete registration of the complex amplitude in the pupil of the imaging system, without any reference beam, provides a convenient link between the experimental and computational optics. Unlike other approaches to digital holography, our method does not require any external reference beam, resulting in a simple and robust registration setup. Computer analysis of the experimentally registered field allows for focusing the image in the whole range from zero to infinity, and for virtual correction of the aberrations present in the real optical system, by applying the adaptive wavefront corrections to its virtual model.
- 22 May 2015
- Published by IOP Publishing in Laser Physics
- Vol. 25 (6) , 065006
Array of unstable resonators for a laser with an annular gain geometry
The application of an array of confocal unstable resonators, formed by spherical mirrors, to a laser with an annular active medium, is considered. The results of numerical simulations show that the proposed design generates high quality output beams in gas and solid-state lasers with low to moderate amplitude of intracavity aberrations.
- 22 May 2015
- Published by IOP Publishing in Laser Physics
- Vol. 25 (6) , 065006
Array of unstable resonators for a laser with an annular gain geometry
The application of an array of confocal unstable resonators, formed by spherical mirrors, to a laser with an annular active medium, is considered. The results of numerical simulations show that the proposed design generates high quality output beams in gas and solid-state lasers with low to moderate amplitude of intracavity aberrations.
- 8 April 2015
- Published by Optica Publishing Group in Optics Letters
- Vol. 40 (8) , 1702-1704
Structured film for compensation of anthropogenic radiative forcing.
A possible solution for the global warming problem consists of scattering the solar radiation by a cloud of screens, placed near the inner Lagrange point (L1), between the sun and the earth. The thin film material for such a screen should have a high longevity in space environment, low areal density, high on-axis scattering, very low integral reflectivity over the whole solar spectrum, and good handling properties such as foldability and robustness. We report on experimental fabrication of free-standing robust nitride films satisfying to most optical and mechanical requirements for such a screen.
- 8 April 2015
- Published by Optica Publishing Group in Optics Letters
- Vol. 40 (8) , 1702-1704
Structured film for compensation of anthropogenic radiative forcing.
A possible solution for the global warming problem consists of scattering the solar radiation by a cloud of screens, placed near the inner Lagrange point (L1), between the sun and the earth. The thin film material for such a screen should have a high longevity in space environment, low areal density, high on-axis scattering, very low integral reflectivity over the whole solar spectrum, and good handling properties such as foldability and robustness. We report on experimental fabrication of free-standing robust nitride films satisfying to most optical and mechanical requirements for such a screen.