IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

Porwal, Prasanna; Pachade, Samiksha; Kokare, Manesh; Deshmukh, Girish; Son, Jaemin; Bae, Woong; Liu, Lihong; Wang, Jianzong; Liu, Xinhui; Gao, Liangxin; Wu, Tian Bo; Xiao, Jing; Wang, Fengyan; Yin, Baocai; Wang, Yunzhi; Danala, Gopichandh; He, Linsheng; Choi, Yoon Ho; Lee, Yeong Chan; Jung, Sang Hyuk; Li, Zhongyu; Sui, Xiaodan; Wu, Junyan; Li, Xiaolong; Zhou, Ting; Tóth, János; Baran, Ágnes; Kori, Avinash; Chennamsetty, Sai Saketh; Safwan, Mohammed; Alex, Varghese; Lyu, Xingzheng; Cheng, Li; Chu, Qinhao; Li, Pengcheng; Ji, Xin; Zhang, Sanyuan; Shen, Yaxin; Dai, Ling; Saha, Oindrila; Sathish, Rachana; Melo, Tânia; Araújo, Teresa; Harangi, Balázs; Sheng, Bin; Fang, Ruogu; Sheet, Debdoot; Hajdú, András; Zheng, Yuanjie; Mendonça, Ana Maria; Zhang, Shaoting; Campilho, Aurélio; Zheng, Bin; Shen, Dinggang; Giancardo, Luca; Quellec, Gwénolé; Mériaudeau, Fabrice

doi:10.1016/j.media.2019.101561

Cited by 191 publications

(110 citation statements)

References 150 publications

(168 reference statements)

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“…This dataset was available as a part of "Diabetic Retinopathy: Segmentation and Grading Challenge (http://biomedicalimaging.org/2018/challenges/)" organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI-2018), Washington D.C. The data challenge was hosted on Grand Challenges in Biomedical Imaging Platform [20]. Information about specifications and data accessibility is provided in the Table 1.…”

Section: Discussionmentioning

confidence: 99%

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Porwal¹,

Pachade²,

Kamble³

et al. 2018

Data

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Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting the working-age population in the world. Recent research has given a better understanding of the requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer-aided disease diagnosis in retinal image analysis could ease mass screening of populations with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. Diverse and representative retinal image sets are essential for developing and testing digital screening programs and the automated algorithms at their core. To the best of our knowledge, IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population. It constitutes typical diabetic retinopathy lesions and normal retinal structures annotated at a pixel level. The dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image. This makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.

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Section: Discussionmentioning

confidence: 99%

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Porwal¹,

Pachade²,

Kamble³

et al. 2018

Data

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516

242

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“…The challenge submission is currently closed. iFLYTEK‐MIG team [40] segmented three lesions simultaneously using Mask‐rcnn [41]. In VRT team [40] method, an edited U‐net architecture is used to segment all four lesions available in the data set.…”

Section: Methodsmentioning

confidence: 99%

“…iFLYTEK‐MIG team [40] segmented three lesions simultaneously using Mask‐rcnn [41]. In VRT team [40] method, an edited U‐net architecture is used to segment all four lesions available in the data set. A patch‐wise approach is used by PATech team [40] to classify lesions with false positives bootstrapping.…”

Section: Methodsmentioning

confidence: 99%

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Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations

et al. 2020

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Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are important indicators of diabetic retinopathy progression. The authors introduce a two‐stage deep learning approach for microaneurysms segmentation using multiple scales of the input with selective sampling and embedding triplet loss. The proposed approach facilitates a region proposal fully convolutional neural network trained on segmented patches and a patch‐wise refinement network for improving the results suggested by the first stage hypothesis. To enhance the discriminative power of the second stage refinement network, the authors use triplet embedding loss with a selective sampling routine that dynamically assigns sampling probabilities to the oversampled class patches. This approach introduces a 23.5% relative improvement over the vanilla fully convolutional neural network on the Indian Diabetic Retinopathy Image Data set segmentation data set. The proposed segmentation is incorporated in a classification model to solve two downstream tasks for diabetic retinopathy detection and referable diabetic retinopathy detection. The classification tasks are trained on the Kaggle diabetic retinopathy challenge data set and evaluated on the Messidor data. The authors show that adding the segmentation enhances the classification performance and achieves comparable performance to the state‐of‐the‐art models.

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“…We would like to acknowledge for the availability of the datasets [6] and [7] that were used in our experimental work. Other references to these datasets include [25] [26] [27] and [28].…”

Section: Acknowledgementsmentioning

confidence: 99%

Segmentation of Diabetic Retinopathy Lesions: The Common Fallacy and Evaluation of Real Segmenters

Furtado¹

2020

OJMI

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In the context of automated analysis of eye fundus images, it is an important common fallacy that prior works achieve very high scores in segmentation of lesions, and that fallacy is fueled by some reviews reporting very high scores, and perhaps some confusion with terms. A simple analysis of the detail of the few prior works that really do segmentation reveals scores between 7% and 70% in sensitivity for 1 FPI. That is clearly sub-par with medical doctors trained to detect signs of Diabetic Retinopathy, since they can distinguish well the contours of lesions in Eye Fundus Images (EFI). Still, a full segmentation of lesions could be an important step for both visualization and further automated analysis using rigorous quantification or areas and numbers of lesions to better diagnose. I discuss what prior work really does, using evidence-based analysis, and confront with segmentation networks, comparing on the terms used by prior work to show that the best performing segmentation network outperforms those prior works. I also compare architectures to understand how the network architecture influences the results. I conclude that, with the correct architecture and tuning, the semantic segmentation network improves up to 20 percentage points over prior work in the real task of segmentation of lesions. I also conclude that the network architecture and optimizations are important factors and that there are still important limitations in current work.

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IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

Cited by 191 publications

References 150 publications

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations

Segmentation of Diabetic Retinopathy Lesions: The Common Fallacy and Evaluation of Real Segmenters

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