Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning.
Automatic segmentation and measurement of the choroid layer is useful in studying of related fundus diseases, such as diabetic retinopathy and high myopia. However, most algorithms are not helpful for choroid layer segmentation due to its blurred boundaries and complex gradients. Therefore, this pap...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 5; pp. 1153 - 1164 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159758920&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159758920 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2022 vid: 35 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159758920 159758920 159758920 10.1007/s10278-021-00571-x 159758920 ppf: 1153 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning. aug: au: Xu, Xiangcong Wang, Xuehua Lin, Jingyi Xiong, Honglian Wang, Mingyi Tan, Haishu Xiong, Ke Han, Dingan affil: School of Physics and Optoelectronic Engineering, Foshan University, Foshan, Guangdong, China sug: subj: Myopia Pathology Uvea Pathology Tomography, Optical Coherence Methods Deep Learning Automation Radiographic Image Enhancement Methods Neural Networks (Computer) Uvea Radiography Human Algorithms Experimental Studies Descriptive Statistics Uvea Anatomy and Histology ab: Automatic segmentation and measurement of the choroid layer is useful in studying of related fundus diseases, such as diabetic retinopathy and high myopia. However, most algorithms are not helpful for choroid layer segmentation due to its blurred boundaries and complex gradients. Therefore, this paper aimed to propose a novel choroid segmentation method that combines image enhancement and attention-based dense (AD) U-Net network. The choroidal images obtained from optical coherence tomography (OCT) are pre-enhanced by algorithms that include flattening, filtering, and exponential and linear enhancement to reduce choroid-independent information. Experimental results obtained from 800 OCT B-scans of the choroid layers from both normal eyes and high myopia showed that image enhancement significantly increased the performance of ADU-Net, with an AUC of 99.51% and a DSC of 97.91%. The accuracy of segmentation using the ADU-Net method with image enhancement is superior to that of the existing networks. In addition, we describe some algorithms that can measure automatically choroidal foveal thickness and the volume of adjacent areas. Statistical analyses of the choroidal parameters variation indicated that compared with normal eyes, high myopia has a reduction of 86.3% of the choroidal foveal thickness and 90% of the adjacent volume. It proved that high myopia is likely to cause choroid layer attenuation. These algorithms would have wide application in the diagnosis and precaution of related fundus lesions caused by choroid thinning from high myopia in future studies. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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