A hybrid framework for traditional and deep learning segmentation methods with feature detection for optical coherence tomography (OCT) images

Primary angle closure disease (PACD) is a leading cause of permanent vision loss worldwide, so early treatment of patients suffering from symptoms of PACD is crucial to prevent vision loss. Gonioscopy is the current clinical standard for diagnosing PACD. However, gonioscopy is a qualitative subjective assessment method. Thus, there is a need for a quantitative method to diagnose PACD. Anterior Segment Optical Coherence Tomography (AS-OCT) is an imaging modality which produces images of anterior structures such as the anterior chamber angle. Adoption of AS-OCT has been slow due to AS-OCT analysis not being standardized and inefficient. Currently, users must annotate each image by hand using proprietary software and use expert knowledge to diagnose PACD based on the key features annotated. Using an imaging-informatics based approach on a dataset of over 900 images we have developed a system to streamline and standardize AS-OCT analysis. This system will be DICOM compatible to promote standardization of AS-OCT images. This system will be attached to a HIPAA compliant database and will require a secure login to protect patient privacy. We have developed a streamlined approach towards annotating key features in AS-OCT images which will be used to validate the results produced by SimpleMind - an open source software framework supporting deep neural networks with machine learning and automatic parameter tuning. SimpleMind is integrated into the system to increase the efficiency of analyzing AS-OCT images and eliminate the need to annotate images for clinical diagnosis.