Automatic Drusen Segmentation for Age-Related Macular Degeneration in Fundus Images Using Deep Learning

Pham, Quang Trung; Ahn, Sangtae; Song, Su Jeong; Shin, Jitae

doi:10.3390/electronics9101617

Cited by 16 publications

(13 citation statements)

References 37 publications

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“…The issue of poor image quality has also been mentioned by other works 38,39 . The pretrained drusen segmentation model that we used was trained on high‐quality CFPs with a resolution of 1200 × 1200 36 …”

Section: Classification Resultsmentioning

confidence: 84%

“…On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.58 F I G U R E 3 Examples of inaccurate drusen masks generated by on the Ocular Disease Intelligent Recognition (ODIR) dataset. 36 Row 1: colour fundus photographs; Row 2: generated drusen masks. We observed that the drusen masks contain many false-positive pixels and may lead models to focus on the wrong regions.…”

Section: Discussionmentioning

confidence: 99%

“…The drusen mask is extracted and used to generate multi-head attention maps for the backbone network shown in blue. 36 Feature maps from the last three Resnet blocks are fused and fed to a fully connected layer for classification. Note: p-Value < 0.05 in bold indicates statistically significant differences between our approach (drusen + LSTM) and other methods.…”

Section: Experiments 1: Evaluation Of the Ocular Disease Intelligent ...mentioning

confidence: 99%

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Drusen‐aware model for age‐related macular degeneration recognition

Pan

et al. 2023

Ophthalmic Physiologic Optic

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Introduction The purpose of this study was to build an automated age‐related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age‐related changes by using drusen masks for spatial feature supervision. Methods A range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre‐processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver‐operating characteristic (AUC). Fivefold cross‐validation was performed, and the results compared with four other methods. Results Excellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01). Conclusion The proposed drusen‐aware model outperformed baseline and other vessel feature‐based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five‐category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real‐life clinical setting.

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Section: Classification Resultsmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Experiments 1: Evaluation Of the Ocular Disease Intelligent ...mentioning

confidence: 99%

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Drusen‐aware model for age‐related macular degeneration recognition

Pan

et al. 2023

Ophthalmic Physiologic Optic

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“…The two sizes were appropriate for completing the network training while occupying fewer computing resources and preserving the features and details of the images to some extent. 25 , 26 Data augmentation was used in the training stages to simulate the uncertainty in the image acquisition process by means of giving the augmented image the same label as the original image.…”

Section: Discussionmentioning

confidence: 99%

An Artificial-Intelligence–Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs

Tang

Yuan

Tian

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose To develop deep learning models based on color fundus photographs that can automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and segment myopia-related lesions. Methods Photographs were graded and annotated by four ophthalmologists and were then divided into a high-consistency subgroup or a low-consistency subgroup according to the consistency between the results of the graders. ResNet-50 network was used to develop the classification model, and DeepLabv3+ network was used to develop the segmentation model for lesion identification. The two models were then combined to develop the classification-and-segmentation–based co-decision model. Results This study included 1395 color fundus photographs from 895 patients. The grading accuracy of the co-decision model was 0.9370, and the quadratic-weighted κ coefficient was 0.9651; the co-decision model achieved an area under the receiver operating characteristic curve of 0.9980 in diagnosing pathologic myopia. The photograph-level F 1 values of the segmentation model identifying optic disc, peripapillary atrophy, diffuse atrophy, patchy atrophy, and macular atrophy were all >0.95; the pixel-level F 1 values for segmenting optic disc and peripapillary atrophy were both >0.9; the pixel-level F 1 values for segmenting diffuse atrophy, patchy atrophy, and macular atrophy were all >0.8; and the photograph-level recall/sensitivity for detecting lacquer cracks was 0.9230. Conclusions The models could accurately and automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and monitor progression of the lesions. Translational Relevance The models can potentially help with the diagnosis, screening, and follow-up for pathologic myopic in clinical practice.

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“…In the end, an FC layer was established that classified input pairs into Wet AMD, Dry AMD, PCV, and nAMD. Another work was proposed, based on drusen segmentation for AMD detection, by Pham et al [143] where they tried to tackle the data imbalance problem, as the number of non-drusen pixels was very high compared to drusen pixels. Their model consists of two networks, one an Image level network that uses a Deeplabv3+ base architecture to generate drusen probability maps and a patch-level network that works on corresponding patch images and their probability maps for final prediction.…”

Section: Amd Diagnosismentioning

confidence: 99%

A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images

et al. 2022

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In recent years, there has been an unprecedented growth in computer vision and deep learning implementation owing to the exponential rise of computation infrastructure. The same was also reflected in retinal image analysis and successful artificial intelligence models were developed for various retinal disease diagnoses using a wide variety of visual markers obtained from eye fundus images. This article presents a comprehensive study of different deep learning strategies employed in recent times for the diagnosis of five major eye diseases, i.e., Diabetic retinopathy, Glaucoma, age-related macular degeneration, Cataract, and Retinopathy of prematurity. This article is organized according to the deep learning implementation process pipeline, where commonly used datasets, evaluation metrics, image pre-processing techniques, and deep learning backbone models are first illustrated followed by an extensive review of different strategies for each of the five mentioned retinal diseases is presented. Finally, this article summarizes eight major research directions available in the field of retinal disease diagnosis and outlines key challenges and future scope for the present research community.

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Automatic Drusen Segmentation for Age-Related Macular Degeneration in Fundus Images Using Deep Learning

Cited by 16 publications

References 37 publications

Drusen‐aware model for age‐related macular degeneration recognition

Drusen‐aware model for age‐related macular degeneration recognition

An Artificial-Intelligence–Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs

A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images

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