Cross-spatial attention guided multi-scale fusion network for precise microaneurysm segmentation

OBJECTIVES: Accurate detection of Microaneurysms (MAs) is crucial for the early diagnosis of diabetic retinopathy (DR). However, due to their small size and low contrast with the background, precisely segmenting them from the background poses significant challenges. METHODS: The Cross-Spatial Attention Embedded Multi-Scale Fusion Network (CSAFNet) is proposed to enhance the segmentation accuracy of MAs in fundus images. Based on the U-Net architecture, an improved ResNet50 is used as the backbone for feature extraction. An Efficient Multi-scale Attention (EMA) module is embedded to capture key features across different spatial locations and scales. Simultaneously, a Multi-Scale Deep Fusion (MSDF) module is designed to integrate contextual features from multiple scales. Additionally, the proposed Balanced Focusing Boundary (BFB) loss function improves segmentation by enhancing geometric accuracy at object boundaries. RESULTS: The method is evaluated on four datasets (E-ophtha, TongjiU-DROD, IDRiD, Messidor) with various advanced methods. On IDRiD, its F1-score and IoU improved by 6.16 and 5.63 % respectively, compared to the state-of-the-art (SOTA). On the Messidor and TongjiU-DROD datasets, it demonstrated comprehensive superiority in sensitivity, specificity, F1-score, and IoU. CONCLUSIONS: The proposed CSAFNet can accurately extract MAs from fundus images, thereby effectively assisting in the precise diagnosis of DR.