Light-weight Retinal Layer Segmentation with Global Reasoning

TL;DR

This paper introduces LightReSeg, a lightweight retinal layer segmentation network for OCT images that leverages multi-scale features and global reasoning to outperform current methods with significantly fewer parameters.

Contribution

The paper proposes a novel lightweight network architecture with multi-scale feature extraction and global reasoning for retinal segmentation, reducing parameters while improving accuracy.

Findings

Achieves better segmentation performance than TransUnet

Uses only 3.3M parameters compared to 105.7M

Effective on multiple OCT datasets

Abstract

Automatic retinal layer segmentation with medical images, such as optical coherence tomography (OCT) images, serves as an important tool for diagnosing ophthalmic diseases. However, it is challenging to achieve accurate segmentation due to low contrast and blood flow noises presented in the images. In addition, the algorithm should be light-weight to be deployed for practical clinical applications. Therefore, it is desired to design a light-weight network with high performance for retinal layer segmentation. In this paper, we propose LightReSeg for retinal layer segmentation which can be applied to OCT images. Specifically, our approach follows an encoder-decoder structure, where the encoder part employs multi-scale feature extraction and a Transformer block for fully exploiting the semantic information of feature maps at all scales and making the features have better global reasoning capabilities, while the decoder part, we design a multi-scale asymmetric attention (MAA) module for preserving the semantic information at each encoder scale. The experiments show that our approach achieves a better segmentation performance compared to the current state-of-the-art method TransUnet with 105.7M parameters on both our collected dataset and two other public datasets, with only 3.3M parameters.