Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks
We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms. The algorithm accurately estimated macular thickness with an error of 14.0 ± 22.1 µm, substantially lower than the error obtained using the other algorithms (42.9 ± 116.0 µm and 27.1 ± 69.3 µm, respectively). These results highlighted the proposed algorithm's capability of modeling the wide variability in retinal appearance and obtained a robust and reliable retina segmentation even in severe pathological cases.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2017 |
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| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
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| Enthalten in: |
Biomedical optics express - 8(2017), 7 vom: 01. Juli, Seite 3292-3316 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Venhuizen, Freerk G [VerfasserIn] |
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| Links: |
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| Themen: |
(100.2960) Image analysis |
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| Anmerkungen: |
Date Revised 01.10.2020 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1364/BOE.8.003292 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM273932268 |
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| 245 | 1 | 0 | |a Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks |
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| 520 | |a We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms. The algorithm accurately estimated macular thickness with an error of 14.0 ± 22.1 µm, substantially lower than the error obtained using the other algorithms (42.9 ± 116.0 µm and 27.1 ± 69.3 µm, respectively). These results highlighted the proposed algorithm's capability of modeling the wide variability in retinal appearance and obtained a robust and reliable retina segmentation even in severe pathological cases | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a (100.2960) Image analysis | |
| 650 | 4 | |a (100.4996) Pattern recognition, neural networks | |
| 650 | 4 | |a (110.4500) Optical coherence tomography | |
| 650 | 4 | |a (170.1610) Clinical applications | |
| 650 | 4 | |a (170.4470) Ophthalmology | |
| 700 | 1 | |a van Ginneken, Bram |e verfasserin |4 aut | |
| 700 | 1 | |a Liefers, Bart |e verfasserin |4 aut | |
| 700 | 1 | |a van Grinsven, Mark J J P |e verfasserin |4 aut | |
| 700 | 1 | |a Fauser, Sascha |e verfasserin |4 aut | |
| 700 | 1 | |a Hoyng, Carel |e verfasserin |4 aut | |
| 700 | 1 | |a Theelen, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Sánchez, Clara I |e verfasserin |4 aut | |
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