Conference proceeding
Classification of range of OCT-angiography capillary density using multichannel deep learning models in diabetic retinopathy, aging macular degeneration, and radiation retinopathy
Vol.13407, pp.134070H-134070H-9
Progress in Biomedical Optics and Imaging
04/04/2025
DOI: 10.1117/12.3046775
Abstract
OCT-angiography (OCTA) is an increasingly important image modality for the diagnosis and management of various retinal vascular diseases. However, manual analysis is time-consuming and subjective. Automated segmentation using deep learning can provide consistent and objective measurements, improving capillary density assessments. Studies have shown that automated quantification of capillary density is beneficial, but these studies have primarily classified areas of retina as having normal or abnormal capillary density, and are unable to classify various degrees of capillary density. In this study, an automated method for segmenting microvascular density regions into four categories (normal, mild, moderate, and severe) was developed using a nnU-Net framework. We proposed four models with different input combinations: Model 1 (OCTA only), Model 2 (OCTA + foveal avascular zone [FAZ]), Model 3 (OCTA + large vessel tree [LVT]), and Model 4 (OCTA + FAZ + LVT) to determine whether increasing the number of inputs improved prediction accuracy. The dataset comprised of 50 training and 47 test images, labeled by two independent experts. Dataset 1 (train: 50, test: 15) was annotated by Expert 1, while Dataset 2 (test: 32) was annotated by both experts. The predicted labels were compared to the manual reference labels using Dice coefficients. Although no significant differences between Expert 1 and the models were identified, visual inspection revealed that Model 4 (3-channel input) occasionally produced more consistent and uniform results. ANOVA tests compared the Dice coefficients for Expert 1, Expert 2, and Model 4 and found significant differences only in the normal category (p-value: 0.036), but Tukey’s HSD test found no significant differences between each pair comparison. This study demonstrates that a three-channel input model (OCTA + FAZ + LVT) provides more objective and consistent segmentation of capillary density in OCTA images. The model’s performance, comparable to human experts, represents a significant advancement in automated OCTA image analysis, offering an alternative to manual assessments.
Details
- Title: Subtitle
- Classification of range of OCT-angiography capillary density using multichannel deep learning models in diabetic retinopathy, aging macular degeneration, and radiation retinopathy
- Creators
- Noriyoshi Takahashi - University of IowaJui-Kai Wang - University of IowaMichelle R. Tamplin - University of IowaElaine M. Binkley - University of IowaMona K. Garvin - University of IowaIsabella M. Grumbach - University of IowaRandy H. Kardon - University of Iowa
- Contributors
- Susan M. Astley (Editor) - University of ManchesterAxel Wismüller (Editor) - University of Rochester
- Resource Type
- Conference proceeding
- Publication Details
- Vol.13407, pp.134070H-134070H-9
- Publisher
- SPIE
- Series
- Progress in Biomedical Optics and Imaging
- DOI
- 10.1117/12.3046775
- ISSN
- 1605-7422
- Grant note
- VA Center for the Prevention and Treatment of Visual Loss, Rehabilitation Research and Development (RRD): I50RX003002, RRD I01RX003797
This work was supported, in part, by the VA Center for the Prevention and Treatment of Visual Loss, Rehabilitation Research and Development (RR&D) I50RX003002, and RR&D I01RX003797.
- Language
- English
- Date published
- 04/04/2025
- Academic Unit
- Electrical and Computer Engineering; Iowa Neuroscience Institute; Cardiovascular Medicine; Radiation Oncology; Internal Medicine; Ophthalmology and Visual Sciences
- Record Identifier
- 9984813317902771
Metrics
1 Record Views