基于通道注意力的上下文编码网络内界膜检测
The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detection
摘要：
视盘分割是基于视网膜图像的青光眼等疾病诊断的重要步骤。内界膜（ILM）是OCT的第一边界，通过梯度边缘信息提取视网膜色素上皮（RPE），定位视盘边界。因此，ILM层分割对于视盘定位具有重要意义。在本文中，我们建立了一个新的视盘为中心的数据集，从20名志愿者和手动注释的ILM边界，在每个OCT扫描地面真相。我们还提出了一个基于通道注意力的上下文编码器网络修改CE-Net分割光盘。它主要包括三个阶段：编码器模块、基于信道注意的上下文编码器模块和解码器模块。最后，我们证明了我们提出的方法在上述数据集上实现了最先进的光盘分割性能。
英文摘要：
The optic disc segmentation is an important step for retinal image-based disease diagnosis such as glaucoma. The inner limiting membrane (ILM) is the first boundary in the OCT, which can help to extract the retinal pigment epithelium (RPE) through gradient edge information to locate the boundary of the optic disc. Thus, the ILM layer segmentation is of great importance for optic disc localization. In this paper, we build a new optic disc centered dataset from 20 volunteers and manually annotated the ILM boundary in each OCT scan as ground-truth. We also propose a channel attention based context encoder network modified from the CE-Net to segment the optic disc. It mainly contains three phases: the encoder module, the channel attention based context encoder module, and the decoder module. Finally, we demonstrate that our proposed method achieves state-of-the-art disc segmentation performance on our dataset mentioned above.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
