New grading criterion for retinal haemorrhages in term newborns based on deep convolutional neural networks
© 2019 Royal Australian and New Zealand College of Ophthalmologists..
BACKGROUND: To define a new quantitative grading criterion for retinal haemorrhages in term newborns based on the segmentation results of a deep convolutional neural network.
METHODS: We constructed a dataset of 1543 retina images acquired from 847 term newborns, and developed a deep convolutional neural network to segment retinal haemorrhages, blood vessels and optic discs and locate the macular region. Based on the ratio of areas of retinal haemorrhage to optic disc, and the location of retinal haemorrhages relative to the macular region, we defined a new criterion to grade the degree of retinal haemorrhages in term newborns.
RESULTS: The F1 scores of the proposed network for segmenting retinal haemorrhages, blood vessels and optic discs were 0.84, 0.73 and 0.94, respectively. Compared with two commonly used retinal haemorrhage grading criteria, this new method is more accurate, objective and quantitative, with the relative location of the retinal haemorrhages to the macula as an important factor.
CONCLUSIONS: Based on a deep convolutional neural network, we can segment retinal haemorrhages, blood vessels and optic disc with high accuracy. The proposed grading criterion considers not only the area of the haemorrhages but also the locations relative to the macular region. It provides a more objective and comprehensive evaluation criterion. The developed deep convolutional neural network offers an end-to-end solution that can assist doctors to grade retinal haemorrhages in term newborns.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:48 |
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| Enthalten in: |
Clinical & experimental ophthalmology - 48(2020), 2 vom: 15. März, Seite 220-229 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Mao, Jianbo [VerfasserIn] |
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| Links: |
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| Themen: |
Deep convolutional neural network |
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| Anmerkungen: |
Date Completed 28.05.2021 Date Revised 28.05.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/ceo.13670 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM302533176 |
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| 245 | 1 | 0 | |a New grading criterion for retinal haemorrhages in term newborns based on deep convolutional neural networks |
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| 500 | |a Date Revised 28.05.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2019 Royal Australian and New Zealand College of Ophthalmologists. | ||
| 520 | |a BACKGROUND: To define a new quantitative grading criterion for retinal haemorrhages in term newborns based on the segmentation results of a deep convolutional neural network | ||
| 520 | |a METHODS: We constructed a dataset of 1543 retina images acquired from 847 term newborns, and developed a deep convolutional neural network to segment retinal haemorrhages, blood vessels and optic discs and locate the macular region. Based on the ratio of areas of retinal haemorrhage to optic disc, and the location of retinal haemorrhages relative to the macular region, we defined a new criterion to grade the degree of retinal haemorrhages in term newborns | ||
| 520 | |a RESULTS: The F1 scores of the proposed network for segmenting retinal haemorrhages, blood vessels and optic discs were 0.84, 0.73 and 0.94, respectively. Compared with two commonly used retinal haemorrhage grading criteria, this new method is more accurate, objective and quantitative, with the relative location of the retinal haemorrhages to the macula as an important factor | ||
| 520 | |a CONCLUSIONS: Based on a deep convolutional neural network, we can segment retinal haemorrhages, blood vessels and optic disc with high accuracy. The proposed grading criterion considers not only the area of the haemorrhages but also the locations relative to the macular region. It provides a more objective and comprehensive evaluation criterion. The developed deep convolutional neural network offers an end-to-end solution that can assist doctors to grade retinal haemorrhages in term newborns | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a deep convolutional neural network | |
| 650 | 4 | |a grading criterion | |
| 650 | 4 | |a macula | |
| 650 | 4 | |a retinal haemorrhages | |
| 700 | 1 | |a Luo, Yuhao |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Kun |e verfasserin |4 aut | |
| 700 | 1 | |a Lao, Jimeng |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Ling'an |e verfasserin |4 aut | |
| 700 | 1 | |a Shao, Yirun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Caiyun |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Mingzhai |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Lijun |e verfasserin |4 aut | |
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