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Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images
Authors:Meng-Xiao Li  Su-Qin Yu  Wei Zhang  Hao Zhou  Xun Xu  Tian-Wei Qian and Yong-Jing Wan
Institution:School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China,Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine,School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China,Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine,Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine,Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine and School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
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.
Keywords:optical coherence tomography images  fluid segmentation  2D fully convolutional network  3D fully convolutional network
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