论文标题
基于SD-OCT图像中自动视网膜内囊肿分割的U-NET模型的可推广方法
A generalizable approach based on U-Net model for automatic Intra retinal cyst segmentation in SD-OCT images
论文作者
论文摘要
视网膜内液或囊肿是黄斑病理的重要症状之一，在OCT图像中有效地可视化。在医学图像处理研究中，已广泛研究了这些异常的自动分割。在本文中，我们提出了一种基于U-NET的新方法，用于跨不同供应商的视网膜内囊肿分割，以改善以前基于深层技术所面临的一些挑战。提出的方法有两个主要步骤：1-先前的信息嵌入和输入数据调整，以及2- IRC分割模型。在第一步中，我们以克服接收数据和学习重要上下文知识的一些网络限制的方式将信息注入网络中。在下一步中，我们在标准U-NET体系结构的编码器和解码器部分之间引入了一个连接模块，该模块将信息从编码器更有效地传输到解码器部分。两个公共数据集，即使用Optima和Kermany来评估所提出的方法。结果表明，该方法是一种有效的IRC分割供应商的方法，其平均骰子值分别在Optima和Kermany数据集上为0.78和0.81。
Intra retinal fluids or Cysts are one of the important symptoms of macular pathologies that are efficiently visualized in OCT images. Automatic segmentation of these abnormalities has been widely investigated in medical image processing studies. In this paper, we propose a new U-Net-based approach for Intra retinal cyst segmentation across different vendors that improves some of the challenges faced by previous deep-based techniques. The proposed method has two main steps: 1- prior information embedding and input data adjustment, and 2- IRC segmentation model. In the first step, we inject the information into the network in a way that overcomes some of the network limitations in receiving data and learning important contextual knowledge. And in the next step, we introduced a connection module between encoder and decoder parts of the standard U-Net architecture that transfers information more effectively from the encoder to the decoder part. Two public datasets namely OPTIMA and KERMANY were employed to evaluate the proposed method. Results showed that the proposed method is an efficient vendor-independent approach for IRC segmentation with mean Dice values of 0.78 and 0.81 on the OPTIMA and KERMANY datasets, respectively.
