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

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Abstract

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 with an 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 is developed for combining local and overall knowledge. By extending feature masking, regularization of repetitive features with fusion for combining channels has been done. The performance of the proposed network is evaluated through the analysis of 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 datasets DRISHTI-GS, RIM-ONE for OD, and OC segmentation. In addition, DRIONS-DB and HRF fundus imaging datasets were analyzed for optimizing the model’s performance based on OD segmentation. DSC metric of methodology achieved 0.97 and 0.96 for DRISHTI-GS and RIM-ONE, respectively. Similarly, IOU measures for DRISHTI-GS and RIM-ONE datasets were 0.96 and 0.93, respectively, for OD measurement. For OC segmentation, DSC and IOU were measured as 0.93 and 0.90 respectively for DRISHTI-GS and 0.83 and 0.82 for RIM-ONE data. The proposed technique improved value of metrics with most of the existing methods in terms of DSC and IOU of the results metric of the experiments for OD and OC segmentation.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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  1. Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India

    Abhilasha Joshi & K. K. Sharma

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  1. Abhilasha Joshi
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  2. K. K. Sharma
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by AJ, KKS. The first draft of the manuscript was written by AJ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Abhilasha Joshi.

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Joshi, A., Sharma, K.K. Graph deep network for optic disc and optic cup segmentation for glaucoma disease using retinal imaging. Phys Eng Sci Med 45, 847–858 (2022). https://doi.org/10.1007/s13246-022-01154-y

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