Embedded residual recurrent network and graph search for the segmentation of retinal layer boundaries in optical coherence tomography
Embedded residual recurrent network and graph search for the segmentation of retinal layer boundaries in optical coherence tomography
复制标题
光学相干断层扫描中视网膜层边界分割的嵌入式残差循环网络和图搜索
DOI：
10.1109/tim.2021.3072121
复制
发表时间：
2021
影响因子：
5.6
通讯作者：
Xieping Gao
中科院分区：
文献类型：
--
作者：
Kai Hu;Dong Liu;Zhineng Chen;Xuanya Li;Yuan Zhang;Xieping Gao
对于各种视网膜疾病的研究，视网膜层的准确定量分析对于评估疾病的严重程度和诊断疾病的进展至关重要。光学相干断层扫描（OCT）图像可以清晰地显示视网膜结构的每一层，并发现细微的早期病变，从而为视网膜疾病的诊断提供了金标准。本文提出了一种基于嵌入式残差递归网络（ERR-Net）和图搜索（GS）的由粗到细的视网膜层边界分割方法。考虑到信息传输的完整性和特征的依赖性，我们首先设计了一种新的端到端残差递归网络来粗略分割视网膜层边界。ERR-Net不仅解决了深度带来的梯度问题，而且充分捕捉了图像的全局空间结构。其次，采用GS算法进行边界连续，使层边界分割结果更加准确。最后，我们在三个公开的数据集上评估了所提出的方法的有效性，并将其与每个数据集上的最新方法进行了比较。定量结果和视觉效果表明，该方法优于最先进的方法在视网膜层边界的OCT图像分割。实验结果还表明，该方法在3种不同大小、特征和分割难度的数据集上均具有良好的算法稳定性。
For the study of various retinal diseases, an accurate quantitative analysis of the retinal layer is essential for assessing the severity of the disease and diagnosing the progression of the disease. Optical coherence tomography (OCT) images can clearly show each layer of the retinal structure and detect subtle early lesions, thus providing a gold standard for the diagnosis of retinal diseases. In this article, we propose a coarse-to-fine retinal layer boundary segmentation method based on the embedded residual recurrent network (ERR-Net) and the graph search (GS). Considering the integrity of information transmission and the dependence of features, we first design a novel end-to-end residual recurrent network to roughly segment the retinal layer boundaries. The proposed ERR-Net not only solves the gradient problem brought by the depth but also fully captures the global spatial structure of the image. Second, we employ a GS algorithm for boundary continuous to make the layer boundary segmentation results more accurate. Finally, we evaluate the effectiveness of the proposed method on three publicly available datasets and compare it with the state-of-the-art methods on each dataset. The quantitative results and visual effects show that the proposed method outperforms the state-of-the-art approaches in the segmentation of retinal layer boundaries in OCT images. Moreover, the results also show that the proposed method has good algorithm stability on three datasets with different sizes, characteristics, and segmentation difficulties.