ArticleInternational journal of ophthalmology2022
Three-dimensional diabetic macular edema thickness maps based on fluid segmentation and fovea detection using deep learning.
Article in International journal of ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Automatic fovea detection and choroid segmentation for choroidal thickness assessment in optical coherence tomography.International journal of ophthalmology · 2024Article
- Measurement method of tear meniscus height based on deep learning.Frontiers in medicine · 2023Article
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers.International journal of ophthalmology · 2023Article
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11 authors.
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Abstract
aimTo 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.
methodsOptical 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.
resultsIn 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.
conclusionCompared 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.
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