PFFNet: A pyramid feature fusion network for microaneurysm segmentation in fundus images

Retinal microaneurysm (MA) is a definite earliest clinical sigh of diabetic retinopathy (DR). Its automatic segmentation is key to realizing intelligent screening for early DR, which can significantly reduce the risk of visual impairment in patients. However, the minute scale and subtle contrast of MAs against the background pose challenges for segmentation. This paper focuses on automatic MA segmentation in fundus images. A novel pyramid feature fusion network (PFFNet) that progressively develops and fuses rich contextual information by integrating two pyramid modules is proposed. Multiple global pyramid scene parsing (GPSP) modules are introduced between the encoder and decoder to provide diverse global contextual information for the decoder through reconstructing skip connections. Additionally, a spatial scale‐aware pyramid (SSAP) module is introduced to dynamically fuse multi‐scale contextual information. This rich contextual information will help to identify MAs from low‐contrast background. Furthermore, to mitigate issue related to category imbalance, a combo loss function is introduced. Finally, to validate the effectiveness of the proposed method, experiments are conducted on two publicly available datasets, IDRiD and DDR, and PFFNet is compared with several state‐of‐the‐art models. The experimental results demonstrate the superiority of our PFFNet in the MA segmentation task. This paper aims to automate the segmentation of MAs from fundus images. A novel pyramid feature fusion network (PFFNet) is propose by integrating two pyramidal modules to incorporate global/multi‐scale context information. Experimental results on two public datasets show that our method is highly competitive with other state‐of‐the‐art methods to help physicians in diagnosis. This study helps physicians with rapid screening for early diabetic retinopathy.