Darooei Reza, Nazari Milad, Kafieh Rahle, Rabbani Hossein
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
J Med Signals Sens. 2023 Aug 31;13(4):253-260. doi: 10.4103/jmss.jmss_52_22. eCollection 2023 Oct-Dec.
Optical coherence tomography (OCT) imaging significantly contributes to ophthalmology in the diagnosis of retinal disorders such as age-related macular degeneration and diabetic macular edema. Both diseases involve the abnormal accumulation of fluids, location, and volume, which is vitally informative in detecting the severity of the diseases. Automated and accurate fluid segmentation in OCT images could potentially improve the current clinical diagnosis. This becomes more important by considering the limitations of manual fluid segmentation as a time-consuming and subjective to error method.
Deep learning techniques have been applied to various image processing tasks, and their performance has already been explored in the segmentation of fluids in OCTs. This article suggests a novel automated deep learning method utilizing the U-Net structure as the basis. The modifications consist of the application of transformers in the encoder path of the U-Net with the purpose of more concentrated feature extraction. Furthermore, a custom loss function is empirically tailored to efficiently incorporate proper loss functions to deal with the imbalance and noisy images. A weighted combination of Dice loss, focal Tversky loss, and weighted binary cross-entropy is employed.
Different metrics are calculated. The results show high accuracy (Dice coefficient of 95.52) and robustness of the proposed method in comparison to different methods after adding extra noise to the images (Dice coefficient of 92.79).
The segmentation of fluid regions in retinal OCT images is critical because it assists clinicians in diagnosing macular edema and executing therapeutic operations more quickly. This study suggests a deep learning framework and novel loss function for automated fluid segmentation of retinal OCT images with excellent accuracy and rapid convergence result.
光学相干断层扫描（OCT）成像在眼科诊断视网膜疾病（如年龄相关性黄斑变性和糖尿病性黄斑水肿）中发挥着重要作用。这两种疾病都涉及液体的异常积聚、位置和体积，这对于检测疾病的严重程度至关重要。OCT图像中的自动且准确的液体分割可能会改善当前的临床诊断。考虑到手动液体分割作为一种耗时且易出错的方法的局限性，这一点变得更加重要。
深度学习技术已应用于各种图像处理任务，并且其在OCT液体分割中的性能已经得到探索。本文提出了一种以U-Net结构为基础的新型自动深度学习方法。修改包括在U-Net的编码器路径中应用Transformer，目的是进行更集中的特征提取。此外，根据经验定制了一个自定义损失函数，以有效地结合适当的损失函数来处理不平衡和噪声图像。采用了Dice损失、焦点Tversky损失和加权二元交叉熵的加权组合。
计算了不同的指标。结果表明，与在图像中添加额外噪声后的不同方法相比，所提出的方法具有较高的准确性（Dice系数为95.52）和鲁棒性（Dice系数为92.79）。
视网膜OCT图像中液体区域的分割至关重要，因为它有助于临床医生诊断黄斑水肿并更快地执行治疗操作。本研究提出了一种深度学习框架和新型损失函数，用于视网膜OCT图像的自动液体分割，具有优异的准确性和快速收敛结果。
