Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole

Authors:
Publisher website
Google Scholar
Abstract
Cystoid macular edema (CME) and macular hole (MH) are the leading causes for visual loss in retinal diseases. The volume of the CMEs can be an accurate predictor for visual prognosis. This paper presents an automatic method to segment the CMEs from the abnormal retina with coexistence of MH in three-dimensional-optical coherence tomography images. The proposed framework consists of preprocessing and CMEs segmentation. The preprocessing part includes denoising, intraretinal layers segmentation and flattening, and MH and vessel silhouettes exclusion. In the CMEs segmentation, a three-step strategy is applied. First, an AdaBoost classifier trained with 57 features is employed to generate the initialization results. Second, an automated shape-constrained graph cut algorithm is applied to obtain the refined results. Finally, cyst area information is used to remove false positives (FPs). The method was evaluated on 19 eyes with coexistence of CMEs and MH from 18 subjects. The true positive volume fraction, FP volume fraction, dice similarity coefficient, and accuracy rate for CMEs segmentation were 81.0%±7.8%, 0.80%±0.63%, 80.9%±5.7%, and 99.7%±0.1%, respectively.
Funding Information
  • Joint Shantou International Eye Center
  • National Basic Research Program of China (2014CB748600)
  • National Basic Research Program (973), the Young Scientist Program Foundation of China (2014CB748600)
  • National Basic Research Program of China (2014CB748600)
  • National Natural Science Foundation of China (81401472)
  • National Natural Science Foundation of China (81401472)
  • National Natural Science Foundation of China (61622114)
  • National Natural Science Foundation of China (61622114)
  • National Natural Science Foundation of China (61401294)
  • National Natural Science Foundation of China (61401294)
  • National Natural Science Foundation of China (61401293)
  • National Natural Science Foundation of China (61401293)
  • National Natural Science Foundation of China (81371629)
  • National Natural Science Foundation of China (81371629)
  • Natural Science Foundation of Jiangsu Province (BK20140052)
  • Natural Science Foundation of the Jiangsu Province (BK20140052)
  • International Cooperation Project of Ministry of Science and Technology of China (2016YFE010770)

This publication has 39 references indexed in Scilit: