{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optic-disc-cup-and-fovea-detection-from","title":"Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder","arxiv_id":null,"date":"2020-11-20","proceeding":null,"authors":["Ravi Kamble","Pranab Samanta","Nitin Singhal"],"abstract":"The accurate detection of retinal structures like an optic disc (OD), cup, and fovea is crucial for the analysis of Age-related Macular Degeneration (AMD), Glaucoma, and other retinal conditions. Most segmentation methods rely on separate detection of these retinal structures due to which a combined analysis for computer-aided ophthalmic diagnosis and screening is challenging. To address this issue, the paper introduces an approach incorporating OD, cup, and fovea analysis together. The paper presents a novel method for the detection of OD with a cup and fovea using modified U-Net++ architecture with the EfficientNet-B4 model as a backbone. The extracted features from the EfficientNet are utilized using skip connections in U-Net++ for precise segmentation. Datasets from ADAM and REFUGE challenges are used for evaluating the performance. The proposed method achieved a success rate of 94.74% and 95.73% dice value for OD segmentation on ADAM and REFUGE data, respectively. For fovea detection, the average Euclidean distance of 26.17 pixels is achieved for the ADAM dataset. The proposed method stood first for OD detection and segmentation tasks in ISBI ADAM 2020 challenge.","url_abs":"https://doi.org/10.1007/978-3-030-63419-3_10","url_pdf":"https://link.springer.com/content/pdf/10.1007%2F978-3-030-63419-3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"fovea-detection","task_name":"Fovea Detection"},{"task_slug":"optic-cup-detection","task_name":"Optic Cup Detection"},{"task_slug":"optic-cup-segmentation","task_name":"Optic Cup Segmentation"},{"task_slug":"optic-disc-detection","task_name":"Optic Disc Detection"},{"task_slug":"optic-disc-segmentation","task_name":"Optic Disc Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"efficientnet","method_name":"EfficientNet"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"rmsprop","method_name":"RMSProp"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"squeeze-and-excitation-block","method_name":"Squeeze-and-Excitation Block"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fovea-detection-on-refuge-challenge","task":"Fovea Detection","dataset":"REFUGE Challenge","model":"EfficientNet+U-Net++","rank_in_archive_order":1,"of":1,"metrics":{"Euclidean Distance (ED)":"35.18"},"uses_additional_data":false},{"leaderboard":"/sota/optic-cup-detection-on-refuge-challenge","task":"Optic Cup Detection","dataset":"REFUGE Challenge","model":"EfficientNet+U-Net++","rank_in_archive_order":1,"of":1,"metrics":{"IoU":"0.8128"},"uses_additional_data":false},{"leaderboard":"/sota/optic-cup-segmentation-on-refuge-challenge","task":"Optic Cup Segmentation","dataset":"REFUGE Challenge","model":"EfficientNet+U-Net++","rank_in_archive_order":1,"of":2,"metrics":{"Dice":"0.8762"},"uses_additional_data":false},{"leaderboard":"/sota/optic-disc-detection-on-refuge-challenge","task":"Optic Disc Detection","dataset":"REFUGE Challenge","model":"EfficientNet+U-Net++","rank_in_archive_order":1,"of":1,"metrics":{"IoU":"0.8847"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}