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Updated: Jan 23, 2026
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
Published on: August 4, 2018
Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation
Kun Gao1, Sijie Niu1, Zexuan Ji2
1Shandong Provincial Key Laboratory of Network based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China.
Background And Objective:
Quantitative assessment of subretinal fluid in spectral domain optical coherence tomography (SD-OCT) images is crucial for the diagnosis of central serous chorioretinopathy. For the subretinal fluid segmentation, the traditional methods need to segment retinal layers and then segment subretinal fluid. The layer segmentation has a high influence on subretinal fluid segmentation, so we aim to develop a deep learning model to segment subretinal fluid automatically without layer segmentation.
Methods:
In this paper, we propose a novel image-to-image double-branched and area-constraint fully convolutional networks (DA-FCN) for segmenting subretinal fluid in SD-OCT images. Firstly, the dataset is extended by mirroring image, which helps to overcome the over-fitting problem in the training stage. Then, double-branched structures are designed to learn the shallow coarse and deep representations from the SD-OCT images. DA-FCN model is directly trained using the image and corresponding pixel-based ground truth. Finally, we introduce a novel supervision mechanism by jointing the area loss LA with the softmax loss LS to learn more representative features.
Results:
The testing dataset with 52 SD-OCT volumes from 35 eyes of 35 patients is used for the evaluation of the proposed algorithm based on the cross-validation method. For the three criterions, including the true positive volume fraction, dice similarity coefficient, and positive predicative value, our method can obtain the results of (1) 94.3, 95.3, and 96.4 for dataset 1; (2) 97.3, 95.3, and 93.4 for dataset 2; (3) 93.0, 92.8, and 92.8 for dataset 3; (4) 89.7, 90.1, and 92.6 for dataset 4.
Conclusion:
In this work, we propose a novel fully convolutional network for the automatic segmentation of the subretinal fluid. By constructing the double branched structures and area constraint term, our method shows higher segmentation accuracy without layer segmentation compared with other methods.
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