Translational vision science & technology · 2025
Automated Detection of Retinal Detachment Using Deep Learning-Based Segmentation on Ocular Ultrasonography Images.
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Caki Onur, Guleser Umit Yasar, Ozkan Dilek, Harmanli Mehmet, Cansiz Selahattin, Kesim Cem, Akcan Rustu Emre, Merdzo Ivan, Hasanreisoglu Murat, Gunduz-Demir Cigdem
Аннотация
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
TRANSLATIONAL RELEVANCE: We have developed a novel deep/machine learning based pipeline that has the potential to significantly improve diagnostic accuracy and accessibility for ocular USG.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
TRANSLATIONAL RELEVANCE: We have developed a novel deep/machine learning based pipeline that has the potential to significantly improve diagnostic accuracy and accessibility for ocular USG.