Analysis of Video Retinal Angiography With Deep Learning and Eulerian Magnification Article Swipe
Sumit Laha
,
Rodney LaLonde
,
Austin E. Carmack
,
Hassan Foroosh
,
John Olson
,
Saad Shaikh
,
Ulaş Bağcı
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fcomp.2020.00024
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fcomp.2020.00024
Objective: The aim of this research is to present a novel computer-aided decision support tool in analyzing, quantifying, and evaluating the retinal blood vessel structure from fluorescein angiogram (FA) videos.Methods: The proposed method consists of three phases: (i) image registration for large motion removal from fluorescein angiogram videos, followed by (ii) retinal vessel segmentation, and lastly, (iii) segmentation-guided video magnification. In the image registration phase, individual frames of the video are spatiotemporally aligned using a novel wavelet-based registration approach to compensate for the global camera and patient motion. In the second phase, a capsule-based neural network architecture is employed to perform the segmentation of retinal vessels for the first time in the literature. In the final phase, a segmentation-guided Eulerian video magnification is proposed for magnifying subtle changes in the retinal video produced by blood flow through the retinal vessels. The magnification is applied only to the segmented vessels, as determined by the capsule network. This minimizes the high levels of noise present in these videos and maximizes useful information, enabling ophthalmologists to more easily identify potential regions of pathology.Results: The collected fluorescein angiogram video dataset consists of 1, 402 frames from 10 normal subjects (prospective study). Experimental results for retinal vessel segmentation show that the capsule-based algorithm outperforms a state-of-the-art convolutional neural networks (U-Net), obtaining a higher dice coefficient (85.94%) and sensitivity (92.36%) while using just 5% of the network parameters. Qualitative analysis of these videos was performed after the final phase by expert ophthalmologists, supporting the claim that artificial intelligence assisted decision support tool can be helpful for providing a better analysis of blood flow dynamics.Conclusions: The authors introduce a novel computational tool, combining a wavelet-based video registration method with a deep learning capsule-based retinal vessel segmentation algorithm and a Eulerian video magnification technique to quantitatively and qualitatively analyze FA videos. To authors' best knowledge, this is the first-ever development of such a computational tool to assist ophthalmologists with analyzing blood flow in FA videos.
Related Topics
Concepts
Magnification
Artificial intelligence
Segmentation
Computer vision
Computer science
Sørensen–Dice coefficient
Noise (video)
Image segmentation
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fcomp.2020.00024
- OA Status
- gold
- Cited By
- 3
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3046268728
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3046268728Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fcomp.2020.00024Digital Object Identifier
- Title
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Analysis of Video Retinal Angiography With Deep Learning and Eulerian MagnificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-07-30Full publication date if available
- Authors
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Sumit Laha, Rodney LaLonde, Austin E. Carmack, Hassan Foroosh, John Olson, Saad Shaikh, Ulaş BağcıList of authors in order
- Landing page
-
https://doi.org/10.3389/fcomp.2020.00024Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fcomp.2020.00024Direct OA link when available
- Concepts
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Magnification, Artificial intelligence, Segmentation, Computer vision, Computer science, Sørensen–Dice coefficient, Noise (video), Image segmentation, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2024: 1, 2023: 1, 2021: 1Per-year citation counts (last 5 years)
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51Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.is_published | True |
| primary_location.raw_source_name | Frontiers in Computer Science |
| primary_location.landing_page_url | https://doi.org/10.3389/fcomp.2020.00024 |
| publication_date | 2020-07-30 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2963384288, https://openalex.org/W2793640035, https://openalex.org/W2051583891, https://openalex.org/W2414079435, https://openalex.org/W2889943009, https://openalex.org/W6751741970, https://openalex.org/W2045227075, https://openalex.org/W1244012395, https://openalex.org/W2288234798, https://openalex.org/W2607238539, https://openalex.org/W2962798686, https://openalex.org/W1982882472, https://openalex.org/W6748823286, https://openalex.org/W2559597482, https://openalex.org/W2607485502, https://openalex.org/W2885577113, https://openalex.org/W2237852679, https://openalex.org/W6629816340, https://openalex.org/W2396893767, https://openalex.org/W2556615514, https://openalex.org/W2119076421, https://openalex.org/W6750207810, https://openalex.org/W2327793514, https://openalex.org/W6696976856, https://openalex.org/W6640054144, https://openalex.org/W6697229132, https://openalex.org/W6784971133, https://openalex.org/W2017470475, https://openalex.org/W2164748764, https://openalex.org/W2167204691, https://openalex.org/W6785915482, https://openalex.org/W2121913681, https://openalex.org/W6749868407, https://openalex.org/W1984554603, https://openalex.org/W2313571009, https://openalex.org/W2087031080, https://openalex.org/W6639824700, https://openalex.org/W6743446608, https://openalex.org/W1579692479, https://openalex.org/W2592848770, https://openalex.org/W2150769593, https://openalex.org/W2566335221, https://openalex.org/W1998391547, https://openalex.org/W2883652558, https://openalex.org/W1492789543, https://openalex.org/W2245054051, https://openalex.org/W2153968339, https://openalex.org/W4242177601, https://openalex.org/W2612690371, https://openalex.org/W4212774754, https://openalex.org/W2747329762 |
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| abstract_inverted_index.videos, | 47 |
| abstract_inverted_index.videos. | 302, 326 |
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| abstract_inverted_index.(92.36%) | 223 |
| abstract_inverted_index.(U-Net), | 214 |
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| corresponding_author_ids | https://openalex.org/A5030188696 |
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