Citation:Li MX,Yu SQ,Zhang W,Zhou H,Xu X,Qian TW,Wan YJ.Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images.Int J Ophthalmol 2019;12(6):1012-1020,doi:10.18240/ijo.2019.06.22
Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images
Received:January 25, 2019  Revised:April 03, 2019
Email this Article  Add to Favorites  Print
DOI:10.18240/ijo.2019.06.22
Key Words:optical coherence tomography images; fluid segmentation; 2D fully convolutional network; 3D fully convolutional network
Fund Project:Supported by National Science Foundation of China (No.81800878); Interdisciplinary Program of Shanghai Jiao Tong University (No.YG2017QN24); Key Technological Research Projects of Songjiang District (No.18sjkjgg24); Bethune Langmu Ophthalmological Research Fund for Young and Middle-aged People (No.BJ-LM2018002J).
                    
AuthorInstitution
Meng-Xiao Li School of Information Science and Engineering, East China University of Science and Technology, Shanghai , China
Su-Qin Yu Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine
Wei Zhang School of Information Science and Engineering, East China University of Science and Technology, Shanghai , China
Hao Zhou Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine
Xun Xu Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine
Tian-Wei Qian Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine
Yong-Jing Wan School of Information Science and Engineering, East China University of Science and Technology, Shanghai , China
Hits: 229
Download times: 32
Abstract:
      AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.

    METHODS: A two-dimensional (2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography (OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional (3D) fully convolutional network for segmentation in the retinal OCT images.

    RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%.

    CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.

PMC FullText Html:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6580226/
PDF Fulltext  Download reader  HTML Fulltext   View/Add Comment