Hard exudates segmentation for retinal fundus images based on longitudinal multi-scale fusion network

Accurate segmentation of hard exudate in fundus images is crucial for early diagnosis of retinal diseases. However, hard exudate segmentation is still a challenge task for accurately detecting small lesions and precisely locating the boundaries of ambiguous lesions. In this paper, the longitudinal multi-scale fusion network (LMSF-Net) is proposed for accurate hard exudate segmentation in fundus images. In this network, an adjacent complementary correction module (ACCM) is proposed on the encoding path for complementary fusion between adjacent encoding features, and a progressive iterative fusion module (PIFM) is designed on the decoding path for fusion between adjacent decoding features. Furthermore, a spatial awareness fusion module (SAFM) is proposed at the end of the decoding path for calibration and aggregation of the two decoding outputs. The proposed method can improve segmentation results of hard exudates with different scales and shapes. The experimental results confirm the superiority of the proposed method for hard exudate segmentation with AUPR of 0.6954, 0.9017, and 0.6745 on the DDR, IDRID, and E-Ophtha EX datasets, respectively.