Deep Learning with Automatic Data Augmentation for Segmenting Schisis Cavities in the Optical Coherence Tomography Images of X-Linked Juvenile Retinoschisis Patients
Xing Wei,
Hui Li,
Tian Zhu,
Wuyi Li,
Yamei Li,
Ruifang Sui
Affiliations
Xing Wei
Department of Ophthalmology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuai Fu Yuan, Beijing 100730, China
Hui Li
Department of Ophthalmology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuai Fu Yuan, Beijing 100730, China
Tian Zhu
Department of Ophthalmology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuai Fu Yuan, Beijing 100730, China
Wuyi Li
Department of Ophthalmology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuai Fu Yuan, Beijing 100730, China
Yamei Li
Department of Ophthalmology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuai Fu Yuan, Beijing 100730, China
Ruifang Sui
Department of Ophthalmology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuai Fu Yuan, Beijing 100730, China
X-linked juvenile retinoschisis (XLRS) is an inherited disorder characterized by retinal schisis cavities, which can be observed in optical coherence tomography (OCT) images. Monitoring disease progression necessitates the accurate segmentation and quantification of these cavities; yet, current manual methods are time consuming and result in subjective interpretations, highlighting the need for automated and precise solutions. We employed five state-of-the-art deep learning models—U-Net, U-Net++, Attention U-Net, Residual U-Net, and TransUNet—for the task, leveraging a dataset of 1500 OCT images from 30 patients. To enhance the models’ performance, we utilized data augmentation strategies that were optimized via deep reinforcement learning. The deep learning models achieved a human-equivalent accuracy level in the segmentation of schisis cavities, with U-Net++ surpassing others by attaining an accuracy of 0.9927 and a Dice coefficient of 0.8568. By utilizing reinforcement-learning-based automatic data augmentation, deep learning segmentation models demonstrate a robust and precise method for the automated segmentation of schisis cavities in OCT images. These findings are a promising step toward enhancing clinical evaluation and treatment planning for XLRS.
