Wu Jun, Liu Shuang, Xiao Zhitao, Zhang Fang, Geng Lei
School of Electronics and Information Engineering, TianGong University, Tianjin, China.
School of Life Sciences, TianGong University, Tianjin, China.
Med Phys. 2022 Nov;49(11):7150-7166. doi: 10.1002/mp.15866. Epub 2022 Aug 3.
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.
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 multi-scale 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 multi-scale 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.
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.
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.
视网膜光学相干断层扫描（OCT）图像中视网膜层和液体病变的分割是临床眼科治疗中视网膜病变筛查和诊断的重要组成部分。我们设计了一种新型网络，用于精确分割视网膜的七个组织层和糖尿病性黄斑水肿（DME）的病变区域，可协助医生对疾病进行定量分析。
在本文中，我们提出了视网膜层黄斑水肿网络（RLMENet）模型，以实现视网膜层和液体的端到端联合分割。该网络采用密集多尺度注意力机制来增强视网膜层和液体细节信息的提取，并实现高效的远程建模，从而扩大感受野并获得多尺度特征。由于设计了更复杂的解码器部分，该部分在解码器端集成了更多低级特征信息，从而提取了更多特征，逐步恢复特征图的分辨率并提高分割精度。
我们使用OCT2017数据集的一部分来训练和验证模型，将数据分为训练集、验证集和测试集，并设置为7:2:1的比例。我们在ISIC2017数据集上评估了我们的方法。实验结果表明，本文设计的RLMENet模型能够在视网膜OCT数据集上准确分割七个视网膜组织层和DME病变。最后，测试集中的MIoU值达到了86.55%。该模型可扩展到其他医学图像分割数据集，以实现更好的分割性能。
所提出的方法优于现有的分割方法，实现了更精细的分割效果，并为临床诊断和治疗提供了辅助分析工具。
