The paper "LightReSeg: Light-Weight Retinal Layer Segmentation with Global Reasoning" addresses the challenge of accurate and efficient retinal layer segmentation from optical coherence tomography (OCT) images, which is crucial for diagnosing ophthalmic diseases. The authors propose LightReSeg, a lightweight network architecture designed to handle the low contrast and blood flow noises common in OCT images. The network follows an encoder-decoder structure, incorporating a Transformer block for global reasoning and a Multi-Scale Asymmetric Attention (MAA) module for preserving semantic information at multiple scales. This approach significantly reduces the number of parameters while maintaining or improving segmentation accuracy compared to state-of-the-art methods like TransUnet. Experimental results on three datasets (Vis-105H, Glaucoma, and DME) demonstrate that LightReSeg achieves the best performance in terms of metrics such as mIoU and mPA, with only 3.3M parameters. The paper also includes ablation studies and qualitative comparisons, highlighting the effectiveness of the proposed modules and the overall performance of LightReSeg.The paper "LightReSeg: Light-Weight Retinal Layer Segmentation with Global Reasoning" addresses the challenge of accurate and efficient retinal layer segmentation from optical coherence tomography (OCT) images, which is crucial for diagnosing ophthalmic diseases. The authors propose LightReSeg, a lightweight network architecture designed to handle the low contrast and blood flow noises common in OCT images. The network follows an encoder-decoder structure, incorporating a Transformer block for global reasoning and a Multi-Scale Asymmetric Attention (MAA) module for preserving semantic information at multiple scales. This approach significantly reduces the number of parameters while maintaining or improving segmentation accuracy compared to state-of-the-art methods like TransUnet. Experimental results on three datasets (Vis-105H, Glaucoma, and DME) demonstrate that LightReSeg achieves the best performance in terms of metrics such as mIoU and mPA, with only 3.3M parameters. The paper also includes ablation studies and qualitative comparisons, highlighting the effectiveness of the proposed modules and the overall performance of LightReSeg.