Retinal Cysts are formed by leakage and accumulation of fluid in the retina due to the incompetence of retinal vasculature. These cystic spaces have significance in several ocular diseases such as age-related macular degeneration, diabetic macular edema, etc. Optical coherence tomography is one of the predominant diagnosing techniques for imaging retinal pathologies. Segmenting and quantification of intraretinal cysts plays the vital role in predicting visual acuity. In literature, several methods have been proposed for automatic segmentation of intraretinal cysts. As cystoid macular edema becomes a major problem to humankind, we need to quantify it accurately and operate it out, else it might cause many problems later on. Though research is being carried out in this area, not much of progress has been made and accuracy achieved so far is 68\% which is very less. Also, the methods depend on the quality of the image and give very low results for high noise images like topcon. This work uses ResNet CNN (Convolutional Neural Network) approach of segmentation by the way of patchwise classification for training on image set from cyst segmentation challenge dataset and testing on test data set given by 2 different graders for all 4 vendors in the challenge. It also compares these methods using first publicly available novel cyst segmentation challenge dataset. The methods were evaluated using quantitative measures to assess their robustness against the challenges of intraretinal cyst segmentation. The results are found to be better than the previous state of the art approaches giving more than 70\% dice coefficient on all vendors irrespective of their quality.

翻译：视网膜囊肿是由于视网膜血管功能不全导致液体渗漏和积聚在视网膜中形成的。这些囊肿空间在多种眼部疾病中具有重要意义，如年龄相关性黄斑变性、糖尿病性黄斑水肿等。光学相干断层扫描是视网膜病变成像的主要诊断技术之一。视网膜内囊肿的分割和量化在预测视力方面起着关键作用。文献中已提出了多种自动分割视网膜内囊肿的方法。由于囊性黄斑水肿已成为人类面临的主要问题，我们需要准确量化并进行手术处理，否则可能导致后续诸多问题。尽管该领域正在进行研究，但进展有限，目前达到的准确率仅为68%，非常低。此外，这些方法依赖于图像质量，对高噪声图像（如Topcon设备采集的图像）结果极差。本研究采用ResNet CNN（卷积神经网络）方法，通过逐块分类方式对囊肿分割挑战数据集中的图像集进行训练，并在挑战中由2位不同标注员提供的所有4种设备测试数据集上进行验证。它还利用首个公开的新型囊肿分割挑战数据集对这些方法进行了比较。通过定量指标评估这些方法对视网膜内囊肿分割挑战的鲁棒性。结果表明，本方法优于先前最先进方法，在所有设备上均获得超过70%的Dice系数，且与图像质量无关。