Multiple instance learning for age-related macular degeneration diagnosis in optical coherence tomography images

摘要

Age-related macular degeneration (AMD) is a major cause of irreversible blindness and loss of vision in people over 50 years old. Fluid (or cyst) regions such as intraretinal fluid (IRF), subretinal fluid (SRF), and sub-retinal pigment epithelium (sub-RPE), have different tissue appearance in Optical Coherence Tomography (OCT) images compared to normal retina tissue and are a defining feature of AMD. However, diagnosis of AMD requires an expert to go through every slice (B-scan) of the OCT volume to find whether it contains fluid. This is a tedious and time consuming task. In this paper, we proposed a new framework to help diagnosing AMD via automatically detecting the B-scan frames containing fluid regions. By converting each slice into a bag of features, we introduce multiple instance learning algorithm for the B-scan classification. Cross validation experiments show that the result of our framework have a good classification accuracy with F-measure above 0.85 and the multiple instance random forest algorithm we proposed outperforms other state-of-the-art algorithms.