Automatic segmentation of multitype retinal fluid from optical coherence tomography images using semisupervised deep learning network.

Background/aims: To develop and validate a deep learning model for automated segmentation of multitype retinal fluid using optical coherence tomography (OCT) images.

Methods: We retrospectively collected a total of 2814 completely anonymised OCT images with subretinal fluid (SRF) and intraretinal fluid (IRF) from 141 patients between July 2018 and June 2020, constituting our in-house retinal OCT dataset. On this dataset, we developed a novel semisupervised retinal fluid segmentation deep network (Ref-Net) to automatically identify SRF and IRF in a coarse-to-refine fashion. We performed quantitative and qualitative analyses on the model's performance while verifying its generalisation ability by using our in-house retinal OCT dataset for training and an unseen Kermany dataset for testing. We also determined the importance of major components in the semisupervised Ref-Net through extensive ablation. The main outcome measures were Dice similarity coefficient (Dice), sensitivity (Sen), specificity (Spe) and mean absolute error (MAE).

Results: Our model trained on a handful of labelled OCT images manifested higher performance (Dice: 81.2%, Sen: 87.3%, Spe: 98.8% and MAE: 1.1% for SRF; Dice: 78.0%, Sen: 83.6%, Spe: 99.3% and MAE: 0.5% for IRF) over most cutting-edge segmentation models. It obtained expert-level performance with only 80 labelled OCT images and even exceeded two out of three ophthalmologists with 160 labelled OCT images. Its satisfactory generalisation capability across an unseen dataset was also demonstrated.

Conclusion: The semisupervised Ref-Net required only la few labelled OCT images to generate outstanding performance in automate segmentation of multitype retinal fluid, which has the potential for providing assistance for clinicians in the management of ocular disease.