Joint optic disk and cup segmentation for glaucoma screening using a region-based deep learning network

Li, Feng; Xiang, Wenjie; Zhang, Lijuan; Pan, WenZhe; Zhang, Xuedian; Jiang, Mawei; Zou, Haidong

doi:10.1038/s41433-022-02055-w

Cited by 6 publications

(10 citation statements)

References 43 publications

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“…Notwithstanding, there are some solutions to overcome these disadvantages of proposed method and improve the RIQA performance. To decrease the FPR caused by inadequate field definition, we could employ another image segmentation module to locate OD region 9 , 31 . If the OD is absent from an image, the RIQA had better reject this image.…”

Section: Discussionmentioning

confidence: 99%

Fractal dimension of retinal vasculature as an image quality metric for automated fundus image analysis systems

Lyu

Jajal

Tahir

et al. 2022

Sci Rep

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Automated fundus screening is becoming a significant programme of telemedicine in ophthalmology. Instant quality evaluation of uploaded retinal images could decrease unreliable diagnosis. In this work, we propose fractal dimension of retinal vasculature as an easy, effective and explainable indicator of retinal image quality. The pipeline of our approach is as follows: utilize image pre-processing technique to standardize input retinal images from possibly different sources to a uniform style; then, an improved deep learning empowered vessel segmentation model is employed to extract retinal vessels from the pre-processed images; finally, a box counting module is used to measure the fractal dimension of segmented vessel images. A small fractal threshold (could be a value between 1.45 and 1.50) indicates insufficient image quality. Our approach has been validated on 30,644 images from four public database.

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Section: Discussionmentioning

confidence: 99%

Fractal dimension of retinal vasculature as an image quality metric for automated fundus image analysis systems

Lyu

Jajal

Tahir

et al. 2022

Sci Rep

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“…Li et al. [44] used ResNet‐34 rather than VGG‐16 to extract features based on the method proposed in [2] and obtained good segmentation performance.…”

Section: Methodologiesmentioning

confidence: 99%

A review of optic disc and optic cup segmentation based on fundus images

Ma,

Cao,

Chen

2024

IET Image Processing

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Optic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic medicine and is crucial for aiding glaucoma screening. With the development of smart healthcare and the increase of large datasets, there is an increasing number of research efforts targeting OD and OC segmentation, making it particularly important to provide a systematic review of the latest advances in the field. This paper presents a systematic review of commonly used datasets, evaluation metrics, and related research results in the field of OD and OC segmentation. The advantages and disadvantages of segmentation techniques based on traditional and deep learning methods are comparatively analysed. In addition, this study emphasizes the importance of OD and OC segmentation efforts in smart healthcare. Despite the technological advances, the lack of generalization capability is still a major obstacle limiting its clinical application. To address this issue, this study explores unsupervised domain adaptation methods to enhance the generalization performance of segmentation techniques and provide new strategies for clinical diagnosis. Finally, this paper discusses the challenges and future research directions faced by OD and OC segmentation when applied in the medical field to help readers comprehensively grasp the research dynamics in this area.

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“…Deep learning approaches 21,22,24,25,[27][28][29][30] Automatic learning of features. Larger datasets are required for improved performance.…”

Section: Time Consumingmentioning

confidence: 99%

Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

Prakash

Kakarla

2023

Int J Imaging Syst Tech

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Fundus disorders like Glaucoma are a serious health risk that affects people all over the world, lowering their quality of life. Optic disc (OD) segmentation is a crucial step for automatic detection of glaucoma with fundus images. This paper presents an efficient and accurate OD segmentation methodology from eye fundus images. In the proposed methodology, fundus images are transformed from RGB (Red, Green, and Blue) color space to LAB (Luminance, A‐axis, B‐axis) color space. A new image is generated using the luminance, A‐axis component, and image information content. As a pre‐segmentation stage, the unsupervised approach extracts the region of interest containing OD. The adaptive threshold method, followed by morphological operations, extracts the contours of the OD region and removes the spur. Segmentation of OD is a challenging task due to similar intensity levels with neighborhood, vascular occlusion, and retinal atrophy. The proposed work overcomes the challenges by segmentation of the OD at three levels, which enhances the functionality at different illumination conditions. The features are extracted at various levels to determine whether the segmented image contains the OD or not. The efficiency of the proposed methodology demonstrates by applying it to a variety of fundus images from datasets, namely ISBI IDRiD, DRISHTI‐GS, and SYSU. Key performance metrics like Intersection over Union and Dice Coefficient are used to evaluate the performance of the proposed methodology. The proposed methodology achieves Intersection over Union of 0.996, 0.997, and 0.994 on ISBI IDRiD, DRISHTI‐GS, and SYSU datasets, respectively. Similarly, the proposed methodology obtains 0.96, 0.97, and 0.98 Dice Coefficient on the three datasets.

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Joint optic disk and cup segmentation for glaucoma screening using a region-based deep learning network

Cited by 6 publications

References 43 publications

Fractal dimension of retinal vasculature as an image quality metric for automated fundus image analysis systems

Fractal dimension of retinal vasculature as an image quality metric for automated fundus image analysis systems

A review of optic disc and optic cup segmentation based on fundus images

Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

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