Abstract
Diabetic retinopathy is a pathological change of the retina that occurs for long-term diabetes. The patients become symptomatic in advanced stages of diabetic retinopathy resulting in severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy stages. There is a need of an automated screening tool for the early detection and treatment of patients with diabetic retinopathy. This paper focuses on the segmentation of red lesions using nested U-Net Zhou et al. (Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, 2018) followed by removal of false positives based on the sub-image classification method. Different sizes of sub-images were studied for the reduction in false positives in the sub-image classification method. The network could capture semantic features and fine details due to dense convolutional blocks connected via skip connections in between down sampling and up sampling paths. False-negative candidates were very few and the sub-image classification network effectively reduced the falsely detected candidates. The proposed framework achieves a sensitivity of \(88.79\%\), precision of \(71.50\%\), and F1-Score of \(79.21\%\) for the DIARETDB1 data set Kalviainen and Uusutalo (Medical Image Understanding and Analysis, Citeseer, 2007). It outperforms the state-of-the-art networks such as U-Net Ronneberger et al. (International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015) and attention U-Net Oktay et al. (Attention u-net: Learning where to look for the pancreas, 2018).
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Data Availability
The study was done using two publicly available datasets MESSIDOR and DIARETDB1.For this type of study, formal consent is not required.
Code Availability
Codes for U-Net++ and ResNet-18are available publicly. For this work, code of UNet ++ was modified.
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The authors contributed to an improved version of U-Net++ with a postprocessing method for further reduction of false positives by sub-image classification approach. Ablation study of nested U-Net was performed to determine its depth. A robust, fast, improved, and effective segmentation of red lesion could be achieved.
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Kundu, S., Karale, V., Ghorai, G. et al. Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives. J Digit Imaging 35, 1111–1119 (2022). https://doi.org/10.1007/s10278-022-00629-4
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DOI: https://doi.org/10.1007/s10278-022-00629-4