Quantification of Parapapillary Atrophy and Optic Disc

Lu, Cheng-Kai; Tang, Tong Boon; Laude, Augustinus; Deary, Ian J.; Dhillon, Baljean; Murray, Alan F.

doi:10.1167/iovs.10-6572

Cited by 13 publications

(13 citation statements)

References 26 publications

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“…Once the PPA region is detected, we then conduct PPA segmentation using a combination of image processing techniques: thresholding, a scanning filter, and multiseed region growing methods. 20 …”

Section: Methodsmentioning

confidence: 99%

“…20 This scheme used an initial segmentation and estimation of the OD-plus-PPA boundary based on a modified Chan-Vase analysis 37 of the blue channel. The OD region is then removed from the OD-plus-PPA using the result obtained from Phase 1, leaving the first order estimation of the PPA region.…”

Section: Phase 3: Ppa Segmentationmentioning

confidence: 99%

“…The actual PPA boundary is subsequently refined by using a multiseed region growing method. 20 PANDORA combines these techniques and exploits both global and local information for PPA and OD segmentation. As before, it could derive the following three physiological parameters (all in pixel):…”

Section: Phase 3: Ppa Segmentationmentioning

confidence: 99%

“…14,20 In addition, PANDORA offers a new feature: it could reveal the shape of PPA, which may be important in understanding the development process of PPA.…”

Section: Phase 3: Ppa Segmentationmentioning

confidence: 99%

“…12 They have decomposed fundus images at multiple resolutions and used Hausdorff-based template matching techniques to detect the OD. More recently, a combination of level set and Chan-Vese (CV) methods has been explored, 20 as CV has the potential to auto-compensate discontinuities in the OD boundary. However, this approach suffers from major drawbacks, e.g., the segmentation process is likely to be time-consuming, CV requires an accurate initial "guess" of the OD boundary, and it is likely to achieve good results only when the OD region is of homogenous intensity.…”

Section: Introductionmentioning

confidence: 99%

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Parapapillary atrophy and optic disc region assessment (PANDORA): retinal imaging tool for assessment of the optic disc and parapapillary atrophy

Tang

Laude

et al. 2012

J. Biomed. Opt

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Abstract. We describe a computer-aided measuring tool, named parapapillary atrophy and optic disc region assessment (PANDORA), for automated detection and quantification of both the parapapillary atrophy (PPA) and the optic disc (OD) regions in two-dimensional color retinal fundus images. The OD region is segmented using a combination of edge detection and ellipse fitting methods. The PPA region is identified by the presence of bright pixels in the temporal zone of the OD, and it is segmented using a sequence of techniques, including a modified Chan-Vese approach, thresholding, scanning filter, and multiseed region growing. PANDORA has been tested with 133 color retinal images (82 with PPA; 51 without PPA) drawn randomly from the Lothian Birth Cohort (LBC) database, together with a "ground truth" estimate from an ophthalmologist. The PPA detection rate is 89.47% with a sensitivity of 0.83 and a specificity of 1. The mean accuracy in defining the OD region is 81.31% (SD ¼ 10.45) when PPA is present and 95.32% (SD ¼ 4.36) when PPA is absent. The mean accuracy in defining the PPA region is 73.57% (SD ¼ 11.62). PANDORA demonstrates for the first time how to quantify the OD and PPA regions using two-dimensional fundus images, enabling ophthalmologists to study ocular diseases related to PPA using a standard fundus camera.

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Section: Methodsmentioning

confidence: 99%

Section: Phase 3: Ppa Segmentationmentioning

confidence: 99%

Section: Phase 3: Ppa Segmentationmentioning

confidence: 99%

“…14,20 In addition, PANDORA offers a new feature: it could reveal the shape of PPA, which may be important in understanding the development process of PPA.…”

Section: Phase 3: Ppa Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parapapillary atrophy and optic disc region assessment (PANDORA): retinal imaging tool for assessment of the optic disc and parapapillary atrophy

Tang

Laude

et al. 2012

J. Biomed. Opt

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Pathological myopia classification with simultaneous lesion segmentation using deep learning

Hemelings

Elen

Blaschko

et al. 2021

Computer Methods and Programs in Biomedicine

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Purpose: To assess the use of deep learning for detection of pathological myopia (PM) and semantic segmentation of myopia-related lesions from fundus images. Methods: This investigation reports on the results of deep learning models developed for the recently introduced Pathological Myopia (PALM) dataset, which consists of 1200 images. Evaluation metrics include area under the receiver operating characteristic curve (AUC) for PM detection, Euclidean distance for fovea localization, and Dice and F1 metrics for the semantic segmentation tasks (optic disc, retinal atrophy and retinal detachment). We also introduce a new Optic Nerve Head (ONH)-based prediction enhancement for the segmentation of atrophy and fovea. Results: Models trained with 400 available training images achieved an AUC of 0.9867 for PM detection, and a Euclidean distance of 58.27 pixels on the fovea localization task, evaluated on a test set of 400 images. Dice and F1 metrics for semantic segmentation of lesions scored 0.9303 and 0.9869 on optic disc, 0.8001 and 0.9135 on retinal atrophy, and 0.8073 and 0.7059 on retinal detachment, respectively. Conclusions: We report a successful approach for a simultaneous classification of pathological myopia and segmentation of associated lesions. Our work was acknowledged with an award in the context of the "Pathological Myopia detection from retinal images" challenge held during the IEEE International Symposium on Biomedical Imaging (April 2019). Considering that (pathological) myopia cases are often identified as false positives and negatives in glaucoma deep learning models, we envision that the current work could aid in future research to discriminate between glaucomatous and highly-myopic eyes, complemented by the localization and segmentation of landmarks such as fovea, optic disc and atrophy.

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Noise reduction for ellipse fitting on medical images

Tang

Laude

et al. 2013

Electron. lett.

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Proposed is a simple yet effective method to reduce noise for ellipse fitting to scattered data on medical images. The method is developed specifically to address the challenge due to variation in the sharpness, magnitude and continuity of edges, when studying the medical images of a large population. The proposed method exploits prior knowledge to eliminate the inhomogeneity within the region of interest, and uses a k-means clustering technique to eliminate background noise. The objective is to create a 'noise-free' edge map for ellipse fitting. The method has been applied to retinal fundus images for optic-disc segmentation. Experiment results demonstrate the benefits of the proposed method.

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Quantification of Parapapillary Atrophy and Optic Disc

Cited by 13 publications

References 26 publications

Parapapillary atrophy and optic disc region assessment (PANDORA): retinal imaging tool for assessment of the optic disc and parapapillary atrophy

Parapapillary atrophy and optic disc region assessment (PANDORA): retinal imaging tool for assessment of the optic disc and parapapillary atrophy

Pathological myopia classification with simultaneous lesion segmentation using deep learning

Noise reduction for ellipse fitting on medical images

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