Automated Detection of Retinal Detachment Using Deep Learning-Based Segmentation on Ocular Ultrasonography Images

This study aims to develop an automated pipeline to detect retinal detachment from B-scan ocular ultrasonography (USG) images by using deep learning-based segmentation.

A computational pipeline consisting of an encoder-decoder segmentation network and a machine learning classifier was developed, trained, and validated using 279 B-scan ocular USG images from 204 patients, including 66 retinal detachment (RD) images, 36 posterior vitreous detachment images, and 177 healthy control images. Performance metrics, including the precision, recall, and F-scores, were calculated for both segmentation and RD detection.

The overall pipeline achieved 96.3% F-score for RD detection, outperforming end-to-end deep learning classification models (ResNet-50 and MobileNetV3) with 94.3% and 95.0% F-scores. This improvement was also validated on an independent test set, where the proposed pipeline led to 96.5% F-score, but the classification models yielded only 62.1% and 84.9% F-scores, respectively. Besides, the segmentation model of this pipeline led to high performances across multiple ocular structures, with 84.7%, 78.3%, and 88.2% F-scores for retina/choroid, sclera, and optic nerve sheath segmentation, respectively. The segmentation model outperforms the standard UNet, particularly in challenging RD cases, where it effectively segmented detached retina regions.

The proposed automated segmentation and classification method improves RD detection in B-scan ocular USG images compared to end-to-end classification models, offering potential clinical benefits in resource-limited settings.

We have developed a novel deep/machine learning based pipeline that has the potential to significantly improve diagnostic accuracy and accessibility for ocular USG.