Retinal Vessel Plexus Differentiation Based on OCT Angiography Using Deep Learning.

PURPOSE

Although structural OCT is traditionally used to differentiate the vascular plexus layers in OCT angiography (OCTA), the vascular plexuses do not always obey the retinal laminations. We sought to segment the superficial, deep, and avascular plexuses from OCTA images using deep learning without structural OCT image input or segmentation boundaries.

DESIGN

Cross-sectional study.

SUBJECTS

The study included 235 OCTA cubes from 33 patients for training and testing of the model.

METHODS

From each OCTA cube, 3 weakly labeled images representing the superficial, deep, and avascular plexuses were obtained for a total of 705 starting images. Images were augmented with standard intensity and geometric transforms, and regions from adjacent plexuses were programmatically combined to create synthetic 2-class images for each OCTA cube. Images were partitioned on a per patient basis into training, validation, and reserved test groups to train and evaluate a U-Net based machine learning model. To investigate the generalization of the model, we applied the model to multiclass thin slabs from OCTA volumes and qualitatively observed the resulting b-scans.

MAIN OUTCOME MEASURES

Plexus segmentation performance was assessed quantitatively using Dice scores on a held-out test set.

RESULTS

After training on single-class plexus images, our model achieved good results (Dice scores > 0.82) and was further improved when using the synthetic 2-class images (Dice >0.95). Although not trained on more complex multiclass slabs, the model performed plexus labeling on slab data, which indicates that the use of only OCTA data shows promise for segmenting the superficial, deep, and avascular plexuses without requiring OCT layer segmentations, and the use of synthetic 2-class images makes a significant performance improvement.

CONCLUSIONS

This study presents the use of OCTA data alone to segment the superficial, deep, and avascular plexuses of the retina, confirming that use of structural OCT layer segmentations as boundaries is not required.

FINANCIAL DISCLOSURES

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.