Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning

Xiao, Qiqi; Zou, Jiaxu; Yang, Muqiao; Gaudio, Alex; Smailagic, Asim; Costa, Pedro; Xu, Min

doi:10.1007/978-3-030-27272-2_29

Cited by 21 publications

(23 citation statements)

References 27 publications

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“…Conditional-GAN 2014 21 Cardiac: [101][102][103][104][105][106][107] Brain: [108][109][110][111][112][113][114] Microscopic: [107,115,116] Orthopedic: [117] Skin: [118,119] Breast: [120] Retina: [121] Brain: [111] Cycle-GAN 2017 09 Microscopic: [122][123][124] Brain: [125][126][127] Cardiac: [128,129] Multi-Organ: [130] Pix2Pix-GAN 2016 07 Multi-Organ: [131] Microscopic: [132] Brain: [133,134] Retina: [135] Liver: [136] Bone: [137] Patch-GAN 2017 04 Retina: [135,138] Bone: [139] Brain: [140] Wasserstein-GAN 2017 02 Breast: [141] Brain: [142] Style-GAN 2019 01 Lung: [143] DC-GAN 2015 01 Skin: [144] metrics are intended to compare segm...…”

Section: Performance Metricsmentioning

confidence: 99%

“…Similarly, CHASEDB1and RIM-ONE are retinal fundus image databases used for segmentation [41,44,[48][49][50]138]. In research [44,46,48,49,51,52,111,121,138], few other datasets are utilized which are Origa650, REFUGE, DRIONS-DB, EyePACS, FGADR, and IDRiD, respectively. Figure 3 shows that ten papers have been published on dermoscopy-related studies.…”

Section: Datasets Based On Rgb Imagingmentioning

confidence: 99%

“…Microscopy studies: [55][56][57][58][59][60][61][62][63][64], [107], [115], [116], [122]-124], [132] Funduscopy studies: [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54], [121], [135], [138] Dermoscopy studies: [78][79][80][81][82][83], [118][119], [144] [29], is introduced to address the computational burden. Attention module is also proposed to select global information from the segmentor network.…”

Section: Rgb Imagingmentioning

confidence: 99%

“…And discriminators play the role of attention to multi scale patterns. Edge detector HEDNet [121] was used for the segmentation task of the diabetic retinopathy dataset. Also, the Fig.…”

Section: Gans Applications In Retina Diseases Segmentationmentioning

confidence: 99%

See 3 more Smart Citations

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

Iqbal

Sharif

Yasmin

et al. 2022

Int J Multimed Info Retr

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Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.

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Section: Performance Metricsmentioning

confidence: 99%

Section: Datasets Based On Rgb Imagingmentioning

confidence: 99%

Section: Rgb Imagingmentioning

confidence: 99%

“…And discriminators play the role of attention to multi scale patterns. Edge detector HEDNet [121] was used for the segmentation task of the diabetic retinopathy dataset. Also, the Fig.…”

Section: Gans Applications In Retina Diseases Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

Iqbal

Sharif

Yasmin

et al. 2022

Int J Multimed Info Retr

View full text Add to dashboard Cite

show abstract

“…Xiao et al 35 presented a novel framework as HEDNet with CGAN, which is widely used in edge detection scheme for the semantic segmentation approach on the IDRiD dataset. It will compute the pixel‐wise annotations to diagnose the four different DR injuries that is, hemorrhages, microaneurysms, soft and hard exudates.…”

Section: Related Workmentioning

confidence: 99%

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

Kaur

2022

Concurrency and Computation

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Segmentation of diabetic retinopathy is a significant requirement for correct severity estimation level. Due to dissimilarity in shapes, size, and quantity of retinal lesions, the procedure of labor-intensive type of grading becomes a more time-consuming and challenging task. It imposes the prerequisite for an automated segmentation framework that can accurately identify the regions with margins and aid ophthalmologists in the early detection and monitoring of the severity level. The proposed work introduces a customized U-Net model based on the InceptionV3 pre-trained CNN network. It uses a periodic shuffling approach along with sub-pixel convolution initialized to convolution nearest-neighbor resize. The model performed training and validation for non-proliferative diabetic retinopathy that is, microaneurysms, hemorrhages, hard and soft exudates on two standard datasets such as IDRiD and DIARETDB1. The proposed model achieves 99.82% accuracy, 88.39% sensitivity, and 99.94% specificity for hemorrhages, 99.95% accuracy, 70.76% sensitivity, and 99.98% specificity for microaneurysms, 99.83% accuracy, 85.9% sensitivity, and 99.95% specificity for hard exudates, along with 99.62% accuracy, 85.38% sensitivity, and 99.71% specificity for soft exudates segmentation on the IDRID dataset. In comparison with both datasets, the proposed system on the IDRID dataset performed accurately with superior state-of-the-art outcomes for segmentation of the retinal lesions.

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A deeply supervised maximum response texton basedSegNetfor simultaneous multi retinal lesion segmentation

Pappu¹,

Krishna²,

Biswal³

et al. 2022

Int J Imaging Syst Tech

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Diabetic Retinopathy (DR) is a diabetic mellitus complication that causes vision impairment and may lead to permanent blindness. The early signs of DR that appear on the retinal surface are microaneurysms, hemorrhages, hard exudates, and soft exudates. Hence the automatic detection of these retinal lesions assists in the early diagnosis of DR. This paper presents a novel deep learning model, MRT‐SegNet (Maximum Response Texton – Segmentation Network) for the automatic segmentation of different retinal lesions simultaneously along with the optic disc. In the proposed MRT‐SegNet, each encoder block consists of an MRT filter bank that extracts the textural feature maps of the retinal images and then fuses them with the local feature maps that are extracted from the traditional encoder block of the network. This fusion enables the network to segment the minute lesions from the retinal surface. The proposed model is evaluated on the IDRiD dataset and achieves a mean Area Under the Precision & Recall Curve (mAUC_PR) of 0.698 and AUC_PR scores of 0.495, 0.706, 0.823, 0.769 for microaneurysms, hemorrhages, hard exudates, and soft exudates respectively. The experimental results demonstrate that the MRT‐SegNet outperformed other multi retinal lesion segmentation models by achieving superior performance.

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Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning

Cited by 21 publications

References 27 publications

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

A deeply supervised maximum response texton basedSegNetfor simultaneous multi retinal lesion segmentation

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