Open AccessThree-dimensional diabetic macular edema thickness maps based on fluid segmentation and fovea detection using deep learning
Author(s) -
Jingjing Xu,
Qijie Wei,
Kang Li,
Zhenping Li,
Yu Tian,
Jianchun Zhao,
Dayong Ding,
Xirong Li,
Guangzhi Wang,
Hong Dai
Publication year - 2022
Publication title -
international journal of ophthalmology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.634
H-Index - 29
eISSN - 2227-4898
pISSN - 2222-3959
DOI - 10.18240/ijo.2022.03.19
Subject(s) - optical coherence tomography , medicine , diabetic macular edema , segmentation , macular edema , artificial intelligence , fovea centralis , ophthalmology , diabetic retinopathy , retinal , retina , gold standard (test) , convolutional neural network , optometry , computer science , foveal , optics , physics , radiology , diabetes mellitus , endocrinology
AIM: To explore a more accurate quantifying diagnosis method of diabetic macular edema (DME) by displaying detailed 3D morphometry beyond the gold-standard quantification indicator-central retinal thickness (CRT) and apply it in follow-up of DME patients.METHODS: Optical coherence tomography (OCT) scans of 229 eyes from 160 patients were collected. We manually annotated cystoid macular edema (CME), subretinal fluid (SRF) and fovea as ground truths. Deep convolution neural networks (DCNNs) were constructed including U-Net, sASPP, HRNetV2-W48, and HRNetV2-W48+Object-Contextual Representation (OCR) for fluid (CME+SRF) segmentation and fovea detection respectively, based on which the thickness maps of CME, SRF and retina were generated and divided by Early Treatment Diabetic Retinopathy Study (ETDRS) grid.RESULTS: In fluid segmentation, with the best DCNN constructed and loss function, the dice similarity coefficients (DSC) of segmentation reached 0.78 (CME), 0.82 (SRF), and 0.95 (retina). In fovea detection, the average deviation between the predicted fovea and the ground truth reached 145.7±117.8 μm. The generated macular edema thickness maps are able to discover center-involved DME by intuitive morphometry and fluid volume, which is ignored by the traditional definition of CRT>250 μm. Thickness maps could also help to discover fluid above or below the fovea center ignored or underestimated by a single OCT B-scan.CONCLUSION: Compared to the traditional unidimensional indicator-CRT, 3D macular edema thickness maps are able to display more intuitive morphometry and detailed statistics of DME, supporting more accurate diagnoses and follow-up of DME patients.