150 citations · 424 across the 27 of their papers we have counts for
14 papers · 1 filter
FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures
Melaku N. Getahun, Oleg Y. Rogov, Dmitry V. Dylov +3
Retinal vascular segmentation, a widely researched topic in biomedical image processing, aims to reduce the workload of ophthalmologists in treating and detecting retinal disorders…
Self-supervised Physics-based Denoising for Computed Tomography
Elvira Zainulina, Alexey Chernyavskiy, Dmitry V. Dylov
Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation, stimulating the development of low-dose CT (LDCT) imaging methods. Lowering the radiation…
PyTorch Image Quality: Metrics for Image Quality Assessment
Sergey Kastryulin, Jamil Zakirov, Denis Prokopenko +1
Image Quality Assessment (IQA) metrics are widely used to quantitatively estimate the extent of image degradation following some forming, restoring, transforming, or enhancing algo…
Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging
Ivan Zakazov, Vladimir Shaposhnikov, Iaroslav Bespalov +1
Generalizability of deep learning models may be severely affected by the difference in the distributions of the train (source domain) and the test (target domain) sets, e.g., when…
Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging
Ekaterina Kuzmina, Artem Razumov, Oleg Y. Rogov +3
Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Aut…
Image Quality Assessment for Magnetic Resonance Imaging
Segrey Kastryulin, Jamil Zakirov, Nicola Pezzotti +1
Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the image quality. The growing popularity of image enhancement, generation, and recovery models…
Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Tasks
Artem Razumov, Oleg Y. Rogov, Dmitry V. Dylov
To accelerate MRI, the field of compressed sensing is traditionally concerned with optimizing the image quality after a partial undersampling of the measurable -space.…
No-reference denoising of low-dose CT projections
Elvira Zainulina, Alexey Chernyavskiy, Dmitry V. Dylov
Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction of the…
Global Adaptive Filtering Layer for Computer Vision
Viktor Shipitsin, Iaroslav Bespalov, Dmitry V. Dylov
We devise a universal adaptive neural layer to "learn" optimal frequency filter for each image together with the weights of the base neural network that performs some computer visi…
Tubular Shape Aware Data Generation for Semantic Segmentation in Medical Imaging
Ilyas Sirazitdinov, Heinrich Schulz, Axel Saalbach +2
Chest X-ray is one of the most widespread examinations of the human body. In interventional radiology, its use is frequently associated with the need to visualize various tube-like…
Deep learning Framework for Mobile Microscopy
Anatasiia Kornilova, Mikhail Salnikov, Olga Novitskaya +5
Mobile microscopy is a promising technology to assist and to accelerate disease diagnostics, with its widespread adoption being hindered by the mediocre quality of acquired images.…
Deep Negative Volume Segmentation
Kristina Belikova, Oleg Rogov, Aleksandr Rybakov +2
Clinical examination of three-dimensional image data of compound anatomical objects, such as complex joints, remains a tedious process, demanding the time and the expertise of phys…
Microscopy Image Restoration with Deep Wiener-Kolmogorov filters
Valeriya Pronina, Filippos Kokkinos, Dmitry V. Dylov +1
Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer fr…
Synthetic CT Generation from MRI Using Improved DualGAN
Denis Prokopenko, Joël Valentin Stadelmann, Heinrich Schulz +2
Synthetic CT image generation from MRI scan is necessary to create radiotherapy plans without the need of co-registered MRI and CT scans. The chosen baseline adversarial model with…