Modified U-Net architecture for semantic segmentation of diabetic retinopathy images

Sambyal, Nitigya; Saini, Poonam; Syal, Rupali; Gupta, Varun

doi:10.1016/j.bbe.2020.05.006

Cited by 104 publications

(46 citation statements)

References 18 publications

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“…To confirm the effectiveness and robustness of our proposed method, we conduct a user study against the state of the art on metrics SEN, IOU, and DICE. The comparative methods include the Dai et al method [ 29 ], Zhang et al method [ 30 ], Van Grinsven et al method [ 17 ], M-Net [ 31 ], FC-DenseNet [ 32 ], Sambyal et al method [ 18 ], and original U-Net. To further show our superiority, we, respectively, display the segmentation quantitative results on four lesion types in Tables 4 and 5 .…”

Section: Resultsmentioning

confidence: 99%

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FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy

Xu¹,

Zhou

Xiao

et al. 2021

BioMed Research International

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Diabetic retinopathy is one of the main causes of blindness in human eyes, and lesion segmentation is an important basic work for the diagnosis of diabetic retinopathy. Due to the small lesion areas scattered in fundus images, it is laborious to segment the lesion of diabetic retinopathy effectively with the existing U-Net model. In this paper, we proposed a new lesion segmentation model named FFU-Net (Feature Fusion U-Net) that enhances U-Net from the following points. Firstly, the pooling layer in the network is replaced with a convolutional layer to reduce spatial loss of the fundus image. Then, we integrate multiscale feature fusion (MSFF) block into the encoders which helps the network to learn multiscale features efficiently and enrich the information carried with skip connection and lower-resolution decoder by fusing contextual channel attention (CCA) models. Finally, in order to solve the problems of data imbalance and misclassification, we present a Balanced Focal Loss function. In the experiments on benchmark dataset IDRID, we make an ablation study to verify the effectiveness of each component and compare FFU-Net against several state-of-the-art models. In comparison with baseline U-Net, FFU-Net improves the segmentation performance by 11.97%, 10.68%, and 5.79% on metrics SEN, IOU, and DICE, respectively. The quantitative and qualitative results demonstrate the superiority of our FFU-Net in the task of lesion segmentation of diabetic retinopathy.

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Section: Resultsmentioning

confidence: 99%

“…Van Grinsven et al [ 17 ] sped up the training by dramatically selecting misclassified negative samples. Sambyal et al [ 18 ] presented a modified U-Net architecture based on the residual network and employ periodic shuffling with subpixel convolution initialized to convolution nearest-neighbor resize.…”

Section: Methodsmentioning

confidence: 99%

FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy

Xu¹,

Zhou

Xiao

et al. 2021

BioMed Research International

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“…E-ophtha is a database of color retinal fundus images used specifically for diabetic retinopathy research. E-ophtha contains two datasets consisting of 463 fundus images that demonstrate either exudates, microaneurysms, or hemorrhages (58). The exudate database contains 47 images with exudates and 35 images with no lesions.…”

Section: Datasets and Research Communities Used For The Development And Training Of Artificial Intelligence Screening Systemsmentioning

confidence: 99%

“…The microaneurysm set contains 148 images with microaneurysms or small hemorrhages and 233 images with no lesions (59). Thus, this dataset is particularly useful for training algorithms to recognize exudates, microaneurysms, and hemorrhages in fundus images (58,59).…”

Section: Datasets and Research Communities Used For The Development And Training Of Artificial Intelligence Screening Systemsmentioning

confidence: 99%

Assessing the Performance of Artificial Intelligence Systems for the Screening of Diabetic Retinopathy: A Systematic Review and Meta-Analysis

Sadjadi¹

2021

Preprint

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Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and one of the leading causes of blindness globally. Due to the progressive nature of the disease, earlier detection and timely treatment can lead to substantial reductions in the incidence of irreversible vision-loss. Artificial intelligence (AI) screening systems have offered clinically acceptable and quicker results in detecting diabetic retinopathy from retinal fundus and optical coherence tomography (OCT) images. Thus, this systematic review and meta-analysis of relevant investigations was performed to document the performance of AI screening systems that were applied to fundus and OCT images of patients from diverse geographic locations including North America, Europe, Africa, Asia, and Australia. A systematic literature search on Medline, Global Health, and PubMed was performed and studies published between October 2015 and January 2020 were included. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and AI-based investigations were mandatory for studies inclusion. The abstracts, titles, and full-texts of potentially eligible studies were screened against inclusion and exclusion criteria. Twenty-one studies were included in this systematic review; 18 met inclusion criteria for the meta-analysis. The pooled sensitivity of the evaluated AI screening systems in detecting diabetic retinopathy was 0.93 (95% CI: 0.92-0.94) and the specificity was 0.88 (95% CI: 0.86-0.89). The included studies detailed training and external validation datasets, criteria for diabetic retinopathy case ascertainment, imaging modalities, DR-grading scales, and compared AI results to those of human graders (e.g., ophthalmologists, retinal specialists, trained nurses, and other healthcare providers) as a reference standard. The findings of this study showed that the majority AI screening systems demonstrated clinically acceptable levels of sensitivity and specificity for detecting referable diabetic retinopathy from retinal fundus and OCT photographs. Further improvement depends on the continual development of novel algorithms with large and gradable sets of images for training and validation. If cost-effectiveness ratios can be optimized, AI can become a financially sustainable and clinically effective intervention that can be incorporated into the healthcare systems of low-to-middle income countries (LMICs) and geographically remote locations. Combining screening technologies with treatment interventions such as anti-VEGF therapy, acellular capillary laser treatment, and vitreoretinal surgery can lead to substantial reductions in the incidence of irreversible vision-loss due to proliferative diabetic retinopathy.

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“…Recently, convolutional neural networks (CNNs) have become widespread in many fields of real life [7][8][9][10][11], numerous deep learning-based methods have been presented for lesion segmentation of DR. The existing methods [12][13][14][15][16][17] for lesion segmentation of DR are categorized into encoderdecoder structures and non-encoder-decoder structures. On the one hand, due to the high resolution of fundus images and GPU memory limitations, works [12][13][14][15] first cropped the original image into patches or resized and input them into U-Net and its variants for lesion segmentation.…”

Section: Introductionmentioning

confidence: 99%

CARNet: Cascade attentive RefineNet for multi-lesion segmentation of diabetic retinopathy images

Guo

Peng

2022

Complex Intell. Syst.

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Diabetic retinopathy is the leading cause of blindness in working population. Lesion segmentation from fundus images helps ophthalmologists accurately diagnose and grade of diabetic retinopathy. However, the task of lesion segmentation is full of challenges due to the complex structure, the various sizes and the interclass similarity with other fundus tissues. To address the issue, this paper proposes a cascade attentive RefineNet (CARNet) for automatic and accurate multi-lesion segmentation of diabetic retinopathy. It can make full use of the fine local details and coarse global information from the fundus image. CARNet is composed of global image encoder, local image encoder and attention refinement decoder. We take the whole image and the patch image as the dual input, and feed them to ResNet50 and ResNet101, respectively, for downsampling to extract lesion features. The high-level refinement decoder uses dual attention mechanism to integrate the same-level features in the two encoders with the output of the low-level attention refinement module for multiscale information fusion, which focus the model on the lesion area to generate accurate predictions. We evaluated the segmentation performance of the proposed CARNet on the IDRiD, E-ophtha and DDR data sets. Extensive comparison experiments and ablation studies on various data sets demonstrate the proposed framework outperforms the state-of-the-art approaches and has better accuracy and robustness. It not only overcomes the interference of similar tissues and noises to achieve accurate multi-lesion segmentation, but also preserves the contour details and shape features of small lesions without overloading GPU memory usage.

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Modified U-Net architecture for semantic segmentation of diabetic retinopathy images

Cited by 104 publications

References 18 publications

FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy

FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy

Assessing the Performance of Artificial Intelligence Systems for the Screening of Diabetic Retinopathy: A Systematic Review and Meta-Analysis

CARNet: Cascade attentive RefineNet for multi-lesion segmentation of diabetic retinopathy images

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