  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.
