Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation

Automated segmentation of diabetic retinopathy (DR) lesions is crucial for assessing DR severity and diagnosis. Most previous segmentation methods overlook the detrimental impact of texture information bias, resulting in suboptimal segmentation results. Additionally, the role of lesion shape is not thoroughly considered. In this paper, we propose a lightweight frequency recalibration network (LFRC-Net) for simultaneous multi-lesion DR segmentation, which integrates a frequency recalibration module into the bottleneck layers of the encoder to analyze texture information and shape features together. The module utilizes a Gaussian pyramid to generate features at different scales, constructs a Laplacian pyramid using a difference of Gaussian filter, and then analyzes object features in different frequency domains with the Laplacian pyramid. The high-frequency component handles texture information, while the low-frequency area focuses on learning the shape features of DR lesions. By adaptively recalibrating these frequency representations, our method can differentiate the objects of interest. In the decoder, we introduce a residual attention module (RAM) to enhance lesion feature extraction and efficiently suppress irrelevant information. We evaluate the proposed model's segmentation performance on two public datasets, IDRiD and DDR, and a private dataset, an ultra-wide-field fundus images dataset. Extensive comparative experiments and ablation studies are conducted across multiple datasets. With minimal model parameters, our approach achieves an mAP_PR of 60.51%, 34.83%, and 14.35% for the segmentation of EX, HE, and MA on the DDR dataset and also obtains excellent results for EX and SE on the IDRiD dataset, which validates the effectiveness of our network.