Content based retrieval of retinal OCT scans using twin CNN

Retinal imaging helps to detect retinal and cardiovascular abnormalities. Among these abnormalities, Diabetic Macular Edema (DME) and Age Related Macular Degeneration (AMD), both are frequent retinal degenerative diseases leading to blindness. Content based retinal OCT scan retrieval process makes use of characteristic features to retrieve similar Optical Coherent Tomography (OCT) scans, index-wise, from a database with minimal human intervention. A number of existing methods take care of segmentation and identification of retinal landmarks and pathologies from OCT volumes. As per the literature survey, till date, no papers are there which deal with the retrieval of retinal OCT scans. In this work, we propose a retrieval system for retinal OCT scans which extracts feature maps of both query and database samples from the layer of deep convolutional neural network and compares for their similarity. The Twin network comparison approach exploits deep features without the resource, space and computation exhaustive network training phase. Most of the techniques involving deep network implementation suffer from the drawbacks of data augmentation and resizing. These requirements have been eliminated automatically as part of the Twin network implementation procedure. The system successfully retrieves retinas with similar symptoms from the database of differently affected and unaffected OCT scans. We evaluated different variations of retrieval performances like AMD-Normal, DME-Normal, AMD-DME, AMD-DME-Normal, etc. Execution time optimization has also been achieved as the network used is comparatively shallow and network training is not required. The system retrieves similar scans from a dataset of abnormal and normal OCT scans with a mean average precision of 0.7571 and mean reciprocal rank of 0.9050. Considering all possible variations of retrieval, we achieved overall mean average precision and mean reciprocal rank of 0.631167 and 0.829607, respectively which are also quite notable with rank thresholds of 3, 5 and 7. Experiments show that the method is noticeably successful in retrieving similar OCT volumes. Per image mean average retrieval time is 8.3 sec. Automatic retrieval of retinal OCT volumes for the presence of a particular ailment can help ophthalmologists in the mass screening process.