Unsupervised deep network for image texture transformation: Improving the quality of cross-correlation analysis and mechanical vortex visualisation during cardiac fibrillation
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Unsupervised deep network for image texture transformation: Improving the quality of cross-correlation analysis and mechanical vortex visualisation during cardiac fibrillation
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| Автор |
Mangileva, D.
Kursanov, A. Katsnelson, L. Solovyova, O. |
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| Тематика |
BIOMECHANICAL DISPLACEMENT FIELD
IMAGE CROSS-CORRELATION ANALYSIS IMAGE TEXTURE TRANSFORMATION UNSUPERVISED DEEP LEARNING |
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| Описание |
Visualisation of cardiac fibrillation plays a very considerable role in cardiophysiological study and clinical applications. One of the ways to obtain the image of these phenomena is the registration of mechanical displacement fields reflecting the track from electrical activity. In this work, we read these fields using cross-correlation analysis from the video of open pig's epicardium at the start of fibrillation recorded with electrocardiogram. However, the quality of obtained displacement fields remains low due to the weak pixels heterogeneity of the frames. It disables to see more clearly such interesting phenomena as mechanical vortexes that underline the mechanical dysfunction of fibrillation. The applying of chemical or mechanical markers to solve this problem can affect the course of natural processes and falsify the results. Therefore, we developed a novel scheme of an unsupervised deep neural network that is based on the state-of-art positional coding technology for a multilayer perceptron. This network enables to generate a couple of frames with a more heterogeneous pixel texture, that is more suitable for cross-correlation analysis methods, from two consecutive frames. The novel network scheme was tested on synthetic pairs of images with different texture heterogeneity and frequency of displacement fields and also it was compared with different filters on our cardiac tissue image dataset. The testing showed that the displacement fields obtained with our method are closer to the ground truth than those which were computed only with the cross-correlation analysis in low contrast images case where filtering is impossible. Moreover, our model showed the best results comparing with the one of the popular filter CLAHE on our dataset. As a result, using our approach, it was possible to register more clearly a mechanical vortex on the epicardium at the start of fibrillation continuously for several milliseconds for the first time. © 2023 The Author(s)
Ministry of Education and Science of the Russian Federation, Minobrnauka; Ural Federal University, UrFU The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University project within the Priority-2030 Program) is gratefully acknowledged. |
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| Дата |
2024-04-05T16:36:41Z
2024-04-05T16:36:41Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Mangileva, D, Kursanov, A, Katsnelson, L & Solovyova, O 2023, 'Unsupervised deep network for image texture transformation: Improving the quality of cross-correlation analysis and mechanical vortex visualisation during cardiac fibrillation', Heliyon, Том. 9, № 11, e22207. https://doi.org/10.1016/j.heliyon.2023.e22207
Mangileva, D., Kursanov, A., Katsnelson, L., & Solovyova, O. (2023). Unsupervised deep network for image texture transformation: Improving the quality of cross-correlation analysis and mechanical vortex visualisation during cardiac fibrillation. Heliyon, 9(11), [e22207]. https://doi.org/10.1016/j.heliyon.2023.e22207 2405-8440 Final All Open Access, Gold, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177189492&doi=10.1016%2fj.heliyon.2023.e22207&partnerID=40&md5=b1113a5533db1746a4fa5f34e4dfc013 http://www.cell.com/article/S240584402309415X/pdf http://elar.urfu.ru/handle/10995/130980 10.1016/j.heliyon.2023.e22207 85177189492 001121582500001 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Elsevier Ltd
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| Источник |
Heliyon
Heliyon |
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