Advanced 3D retinal lesion segmentation using channel-spatial attention-guided multi-scale feature aggregation

Diabetic macular edema (DME) and age-related macular degeneration (AMD) have emerged as leading causes of vision impairment worldwide; optical coherence tomography (OCT) has proven to be a crucial diagnostic tool for these diseases, for its rapid, non-invasive high-resolution imaging of retinal structures. Further accurate assessment of lesions in retinal OCT images plays a pivotal role in the early diagnosis of DME and AMD. However, current diagnosing of AMD and DME is restricted to utilizations of OCT two-dimensional images, for crucial three-dimensional (3D) lesion information inherent in OCT 3D images cannot be effectively extracted and utilized without appropriate methods. Here, we proposed an innovative deep-learning network characterized by fusing multi-scale feature extraction-aggregation and channel-spatial joint attention for high-accuracy 3D lesion segmentations of DME and AMD. Extensive experiment results demonstrated that our proposed method has commendable 3D segmentation performances and robust generalization capabilities, probably helping to understand DME and AMD diseases better and providing great convenience for clinical diagnosis and treatment.