A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

Veena, H.N.; Muruganandham, A.; Kumaran, T. Senthil

doi:10.1007/s42452-020-03221-z

Cited by 25 publications

(20 citation statements)

References 58 publications

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“… 3 , 4 One strong risk factor for glaucoma is a suspicious optic nerve head appearance with abnormal cupping or an increase in the cup-to-disc ratio. 12 , 13 With demographic changes in industrialized countries, the incidence of glaucoma is increasing. 14 , 15 As this eye disease is one of the most common causes of blindness, 16 , 17 cost-effective, rapid, and precise screening is a key factor in rapid medical intervention.…”

Section: Introductionmentioning

confidence: 99%

3D retinal imaging and measurement using light field technology

et al. 2021

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. Significance: Light-field fundus photography has the potential to be a new milestone in ophthalmology. Up-to-date publications show only unsatisfactory image quality, preventing the use of depth measurements. We show that good image quality and, consequently, reliable depth measurements are possible, and we investigate the current challenges of this novel technology. Aim: We investigated whether light field (LF) imaging of the retina provides depth information, on which structures the depth is estimated, which illumination wavelength should be used, whether deeper layers are measurable, and what kinds of artifacts occur. Approach: The technical setup, a mydriatic fundus camera with an LF imager, and depth estimation were validated by an eye model and in vivo measurements of three healthy subjects and three subjects with suspected glaucoma. Comparisons between subjects and the corresponding optical coherence tomography (OCT) measurements were used for verification of the depth estimation. Results: This LF setup allowed for three-dimensional one-shot imaging and depth estimation of the optic disc with green light. In addition, a linear relationship was found between the depth estimates of the OCT and those of the setup developed here. This result is supported by the eye model study. Deeper layers were not measurable. Conclusions: If image artifacts can be handled, LF technology has the potential to help diagnose and monitor glaucoma risk at an early stage through a rapid, cost-effective one-shot technology.

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Section: Introductionmentioning

confidence: 99%

3D retinal imaging and measurement using light field technology

et al. 2021

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“…The macula receives images and light signals. In the central part of the macula, there is a dark brown or red fovea that does not contain any blood vessels [10]. AMD begins with characteristic yellow deposits (drusen) in the macula [8].…”

Section: B Key Retiinal Image Features and Pathologiesmentioning

confidence: 99%

An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation

et al. 2023

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Detecting eye diseases early can make a difference when trying to treat them. Existing diagnostic systems are not only prone to inaccurate judgments, but are also difficult and require a longer time from experts. Artificial intelligence (AI) based on deep learning (DL) has attracted global interest recently because of its effectiveness and accuracy in detecting eye diseases. There are several challenges in diagnosing eye diseases based on retinal fundus imaging. Most of the previous models in the literature targeted these challenges and tried to improve the evaluation metrics, whereas very little attention has been given to reducing the computational complexity of the developed model. The reduced computational complexity is highly desired, as the aim of developing an AI-enabled automated diagnostic system is to use for population-scale screening programs. This observation motivated us, and this work aimed to use a lightweight DL model based on ColonSegNet for retinal vessel segmentation. The performance of the model was assessed using three open-access fundus image datasets: DRIVE, CHASE_DB1, and STARE. The proposed method achieved sensitivity, specificity, accuracy, AUC, and MCC performance of (0.8491, 0.9774, 0.9659, 0.9850, and 0.7960), (0.8607, 0.9806,0.9731, 0.9869, and 0.8014), and (0.8573, 0.9813, 0.9719, 0.9873, and 0.8069) respectively. Furthermore, it has five M trainable parameters, making it lightweight and capable of deployment on low-end hardware devices. These results outperform several lightweight and computationally heavy methods. The reduced number of parameters, computational complexity, and improved segmentation performance support its use in automated diagnostic systems for retinal vessel segmentation.

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“…Among many CNNs-based segmentation approaches, FCN [19], Unet [5], and Auto-encoder-like architecture have become the desired models for MIS. Particularly, such methods achieved impressive performance in brain tumor [15,16], liver tumor [2,18], optic disc [23,28], retina [17], lung [26,12], and cell [8,20]. However, CNNs are limited in their mechanism of aggregating data at pooling layers.…”

Section: Introductionmentioning

confidence: 99%

3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation

Nguyen¹,

Hua²,

Le³

2022

Preprint

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Medical image segmentation has been so far achieving promising results with Convolutional Neural Networks (CNNs). However, it is arguable that in traditional CNNs, its pooling layer tends to discard important information such as positions. Moreover, CNNs are sensitive to rotation and affine transformation. Capsule network is a data-efficient network design proposed to overcome such limitations by replacing pooling layers with dynamic routing and convolutional strides, which aims to preserve the part-whole relationships. Capsule network has shown a great performance in image recognition and natural language processing, but applications for medical image segmentation, particularly volumetric image segmentation, has been limited. In this work, we propose 3D-UCaps, a 3D voxel-based Capsule network for medical volumetric image segmentation. We build the concept of capsules into a CNN by designing a network with two pathways: the first pathway is encoded by 3D Capsule blocks, whereas the second pathway is decoded by 3D CNNs blocks. 3D-UCaps, therefore inherits the merits from both Capsule network to preserve the spatial relationship and CNNs to learn visual representation. We conducted experiments on various datasets to demonstrate the robustness of 3D-UCaps including iSeg-2017, LUNA16, Hippocampus, and Cardiac, where our method outperforms previous Capsule networks and 3D-Unets. Our code is available at https://github.com/VinAIResearch/3D-UCaps.

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A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

Cited by 25 publications

References 58 publications

3D retinal imaging and measurement using light field technology

3D retinal imaging and measurement using light field technology

An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation

3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation

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