Purpose: The segmentation of retinal layers and fluid lesions on retinal optical coherence tomography (OCT) images is an important component of screening and diagnosing retinopathy in clinical ophthalmic treatment. We designed a novel network for accurate segmentation of the seven tissue layers of the retina and lesion areas of diabetic macular edema (DME), which can assist doctors to quantitatively analyze the disease.
Methods: In this article, we propose the Retinal Layer Macular Edema Network (RLMENet) model to achieve end-to-end joint segmentation of retinal layers and fluids. The network employs dense multiscale attention to enhance the extraction of retinal layer and fluid detail information and achieve efficient long-range modeling, which improves the receptive field and obtains multiscale features. As the more complex decoder part is designed, which integrates more low-level feature information on the decoder side, more features are extracted to gradually restore the resolution of the feature map and improve the segmentation accuracy.
Results: We used part of the OCT2017 dataset to train and verify the model to divide the data into a training set, validation set, and test set and set it to a 7:2:1 ratio. We evaluated our method on the ISIC2017 dataset. Experimental results showed that the RLMENet model designed in this work can accurately segment seven retinal tissue layers and DME lesions on the retinal OCT dataset. Finally, the MIoU value in the test set reached 86.55%. The model can be extended to other medical image segmentation datasets to achieve better segmentation performance.
Conclusions: The proposed method was superior to the existing segmentation methods, achieved a more refined segmentation effect and provided an auxiliary analysis tool for clinical diagnosis and treatment. This article is protected by copyright. All rights reserved.
Keywords: convolutional neural networks; multi-scale features; retinal OCT images; semantic segmentation.
This article is protected by copyright. All rights reserved.