Graph deep network for optic disc and optic cup segmentation for glaucoma disease using retinal imaging

摘要

The fundus imaging method of eye screening detects eye diseases by segmenting the optic disc (OD) and optic cup (OC).OD and OC are still challenging to segment accurately. This work proposes three-layer graph-based deep architecture withan enhanced fusion method for OD and OC segmentation. CNN encoder-decoder architecture, extended graph network,and approximation via fusion-based rule are explored for connecting local and global information. A graph-based model isdeveloped for combining local and overall knowledge. By extending feature masking, regularization of repetitive features withfusion for combining channels has been done. The performance of the proposed network is evaluated through the analysisof different metric parameters such as dice similarity coefficient (DSC), intersection of union (IOU), accuracy, specificity,sensitivity. Experimental verification of this methodology has been done using the four benchmarks publicly available datasetsDRISHTI-GS, RIM-ONE for OD, and OC segmentation. In addition, DRIONS-DB and HRF fundus imaging datasets wereanalyzed for optimizing the model’s performance based on OD segmentation. DSC metric of methodology achieved 0.97and 0.96 for DRISHTI-GS and RIM-ONE, respectively. Similarly, IOU measures for DRISHTI-GS and RIM-ONE datasetswere 0.96 and 0.93, respectively, for OD measurement. For OC segmentation, DSC and IOU were measured as 0.93 and 0.90respectively for DRISHTI-GS and 0.83 and 0.82 for RIM-ONE data. The proposed technique improved value of metrics withmost of the existing methods in terms of DSC and IOU of the results metric of the experiments for OD and OC segmentation.