Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers
© 2023 by the American Academy of Ophthalmology. This is..
Purpose: To develop a fully-automatic hybrid algorithm to jointly segment and quantify biomarkers of polypoidal choroidal vasculopathy (PCV) on indocyanine green angiography (ICGA) and spectral domain-OCT (SD-OCT) images.
Design: Evaluation of diagnostic test or technology.
Participants: Seventy-two participants with PCV enrolled in clinical studies at Singapore National Eye Center.
Methods: The dataset consisted of 2-dimensional (2-D) ICGA and 3-dimensional (3-D) SD-OCT images which were spatially registered and manually segmented by clinicians. A deep learning-based hybrid algorithm called PCV-Net was developed for automatic joint segmentation of biomarkers. The PCV-Net consisted of a 2-D segmentation branch for ICGA and 3-D segmentation branch for SD-OCT. We developed fusion attention modules to connect the 2-D and 3-D branches for effective use of the spatial correspondence between the imaging modalities by sharing learned features. We also used self-supervised pretraining and ensembling to further enhance the performance of the algorithm without the need for additional datasets. We compared the proposed PCV-Net to several alternative model variants.
Main Outcome Measures: The PCV-Net was evaluated based on the Dice similarity coefficient (DSC) of the segmentations and the Pearson's correlation and absolute difference of the clinical measurements obtained from the segmentations. Manual grading was used as the gold standard.
Results: The PCV-Net showed good performance compared to manual grading and alternative model variants based on both quantitative and qualitative analyses. Compared to the baseline variant, PCV-Net improved the DSC by 0.04 to 0.43 across the different biomarkers, increased the correlations, and decreased the absolute differences of clinical measurements of interest. Specifically, the largest average (mean ± standard error) DSC improvement was for intraretinal fluid, from 0.02 ± 0.00 (baseline variant) to 0.45 ± 0.06 (PCV-Net). In general, improving trends were observed across the model variants as more technical specifications were added, demonstrating the importance of each aspect of the proposed method.
Conclusion: The PCV-Net has the potential to aid clinicians in disease assessment and research to improve clinical understanding and management of PCV.
Financial Disclosures: Proprietary or commercial disclosure may be found after the references.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:3 |
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| Enthalten in: |
Ophthalmology science - 3(2023), 3 vom: 10. Sept., Seite 100292 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Loo, Jessica [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Revised 10.06.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.xops.2023.100292 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM355306131 |
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| 100 | 1 | |a Loo, Jessica |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Revised 10.06.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023 by the American Academy of Ophthalmology. This is. | ||
| 520 | |a Purpose: To develop a fully-automatic hybrid algorithm to jointly segment and quantify biomarkers of polypoidal choroidal vasculopathy (PCV) on indocyanine green angiography (ICGA) and spectral domain-OCT (SD-OCT) images | ||
| 520 | |a Design: Evaluation of diagnostic test or technology | ||
| 520 | |a Participants: Seventy-two participants with PCV enrolled in clinical studies at Singapore National Eye Center | ||
| 520 | |a Methods: The dataset consisted of 2-dimensional (2-D) ICGA and 3-dimensional (3-D) SD-OCT images which were spatially registered and manually segmented by clinicians. A deep learning-based hybrid algorithm called PCV-Net was developed for automatic joint segmentation of biomarkers. The PCV-Net consisted of a 2-D segmentation branch for ICGA and 3-D segmentation branch for SD-OCT. We developed fusion attention modules to connect the 2-D and 3-D branches for effective use of the spatial correspondence between the imaging modalities by sharing learned features. We also used self-supervised pretraining and ensembling to further enhance the performance of the algorithm without the need for additional datasets. We compared the proposed PCV-Net to several alternative model variants | ||
| 520 | |a Main Outcome Measures: The PCV-Net was evaluated based on the Dice similarity coefficient (DSC) of the segmentations and the Pearson's correlation and absolute difference of the clinical measurements obtained from the segmentations. Manual grading was used as the gold standard | ||
| 520 | |a Results: The PCV-Net showed good performance compared to manual grading and alternative model variants based on both quantitative and qualitative analyses. Compared to the baseline variant, PCV-Net improved the DSC by 0.04 to 0.43 across the different biomarkers, increased the correlations, and decreased the absolute differences of clinical measurements of interest. Specifically, the largest average (mean ± standard error) DSC improvement was for intraretinal fluid, from 0.02 ± 0.00 (baseline variant) to 0.45 ± 0.06 (PCV-Net). In general, improving trends were observed across the model variants as more technical specifications were added, demonstrating the importance of each aspect of the proposed method | ||
| 520 | |a Conclusion: The PCV-Net has the potential to aid clinicians in disease assessment and research to improve clinical understanding and management of PCV | ||
| 520 | |a Financial Disclosures: Proprietary or commercial disclosure may be found after the references | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a OCT | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a indocyanine green angiography | |
| 650 | 4 | |a multimodal image analysis | |
| 650 | 4 | |a polypoidal choroidal vasculopathy | |
| 700 | 1 | |a Teo, Kelvin Y C |e verfasserin |4 aut | |
| 700 | 1 | |a Vyas, Chinmayi H |e verfasserin |4 aut | |
| 700 | 1 | |a Jordan-Yu, Janice Marie N |e verfasserin |4 aut | |
| 700 | 1 | |a Juhari, Amalia B |e verfasserin |4 aut | |
| 700 | 1 | |a Jaffe, Glenn J |e verfasserin |4 aut | |
| 700 | 1 | |a Cheung, Chui Ming Gemmy |e verfasserin |4 aut | |
| 700 | 1 | |a Farsiu, Sina |e verfasserin |4 aut | |
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