arXiv subjects

Anna Andreychenko

Publications and source records attributed to Anna Andreychenko.

3 recordsLinked to original sources

arXiv2023

Platform for generating medical datasets for machine learning in public health

Currently, there are many difficulties regarding the interoperability of medical data and related population data sources. These complications get in the way of the generation of high-quality data sets at city, region and national levels. Moreover, the collection of datasets within large medical centers is feasible due to own IT departments whereas the collection of raw medical data from multiple organizations is a more complicated process. In these circumstances, the most appropriate option is to develop digital products based on microservice architecture. Because of this approach, it is possible to ensure the multimodality of the system, the flexibility of the interface and the internal system approach, when interconnected elements behave as a whole, demonstrating behavior different from the behavior when working independently. These conditions allow, in turn, to ensure the maximum number and representativeness of the resulting data sets. This paper demonstrates a concept of the platform for a sustainable generation of quality and reliable sets of multimodal medical data. It collects data from different external sources, harmonizes it using a special service, anonymizes harmonized data, and labels processed data. The proposed system aims to be a promising solution to the improvement of medical data quality for machine learning.

cs.CY↗

arXiv2018

Deep learning-based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in twenty multi-slice MRI datasets acquired with two different coils in eleven subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy.

physics.med-ph↗

arXiv2018

Ultrahigh field MR-imaging: new frontiers and possibilities

Increasing the static magnetic field strength into the realm of ultrahigh fields (7 T and higher) is the central trend in modern magnetic resonance (MR) imaging. The use of ultrahigh fields in MR-imaging leads to numerous effects some of them raising the image quality, some degrading, some previously undetected in lower fields. This review aims to outline the main consequences of introducing ultrahigh fields in MR-imaging, including new challenges and the proposed solutions, as well as new scanning possibilities unattainable at lower field strengths (below 7 T).

physics.med-ph↗