# The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detection

## TL;DR

This paper introduces a novel channel attention-based context encoder network for segmenting the inner limiting membrane in OCT images, aiding optic disc localization crucial for retinal disease diagnosis.

## Contribution

The paper presents a new deep learning model with channel attention mechanisms for improved ILM segmentation in OCT images, along with a newly created annotated dataset.

## Key findings

- Achieved state-of-the-art segmentation performance on the proposed dataset.
- Demonstrated effectiveness of channel attention in enhancing segmentation accuracy.
- Provided a new dataset for ILM boundary annotation in OCT scans.

## Abstract

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.