The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detection
This work addresses optic disc localization for retinal disease diagnosis, but it is incremental as it modifies an existing network for a specific dataset.
The paper tackled optic disc segmentation in retinal OCT images by proposing a channel attention based context encoder network, achieving state-of-the-art performance on a new dataset of 20 volunteers.
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.