Segmentation of Choroidal Neovascularization lesions in fluorescein angiograms using parametric modeling of the intensity variation

Fahmy, Ahmed S.; Abdelmoula, Walid M.; Mahfouz, Ahmed Essam; Shah, Syed M. Zafi S.

doi:10.1109/isbi.2011.5872494

Cited by 5 publications

(6 citation statements)

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“…Leakage characterization algorithms can be divided into two main categories: comparative methods and saliency methods [24,25]. Comparative methods can be further divided into image subtraction approaches [9,26] or classification approaches [27][28]. ( ), ( ) are entropies of the images X and Y respectively and ( , ) is joint entropy.…”

Section: Leakage Assessment: a Literature Reviewmentioning

confidence: 99%

“…M. S. El-Shahawy et al [26] and A. S. Fahmy et al [27] estimated the relative translation and rotation between the images using (rigid) phase correlation method. Phase correlation is a Fourier-based technique.…”

Section: Image Registrationmentioning

confidence: 99%

“…A. S. Fahmy et al [27] extracted three features of each coordinate of the image along the FA sequence to classify the FA image into three types: background, occult, and classic lesions.…”

Section: Segmentation In Classification Approachesmentioning

confidence: 99%

“…In H. Rabbani et al[9] and M. S. El-Shahawy et al[26], the detection areas of leakage are achieved in a rectangular region centered at the macula and performance metrics have low values. A. S. Fahmy et al[27] study does not allow the leakage characterization in the whole image too. Moreover, it uses unsupervised classification, so it is not very robust to the variations of image intensity and occurrences of lesions that overlap vessels.…”

mentioning

confidence: 99%

“…12 features: the intensity value and the intensity change in 5 time intervals + the slope and intercept of the straight line from linear least-squares regression of the time-intensity data points from the entire sequence Accuracy: 0,751A. S. Fahmy etal [27]. Unsupervised fuzzy C-means 3 features: the time-to-peak intensity, the intensity of the second dye circulation and the normalized difference between the intensity of the first and the third circulations of the dye n.a.Y.…”

mentioning

confidence: 99%

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Quantitative Assessment of Central Serous Chorioretinopathy in Angiographic Sequences of Retinal Images

Ferreira

Penas

Silva

et al. 2019

2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

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Central serous chorioretinopathy is a retinal disease in which there is a leakage of fluid into the subretinal space through a retinal pigment epithelium lesion that may cause a serous detachment of the neurosensory retina. Fluorescein angiography images allow the identification of these leaks. In this type of images, the lesions and the blood vessels appear bright and the remaining anatomical structures of the retina appear dark. The area related to the leakage increases throughout the angiographic sequences and, in general, the leakage can only be visualized completely in the later phases of the exam. In this work, computational methods of image processing and image analysis are used for the detection, characterization, and determination of the size progression of dye leaks along the angiographic sequences. These methods were integrated into a computer-aided diagnosis tool. To the best of our knowledge, a computer-aided diagnosis tool that allows the automatic characterization of leakage of central serous chorioretinopathy in fluorescein angiography images is not described in the literature. The central serous chorioretinopathy leakage segmentation problem is similar to leakage diseases like the diabetic macular edema, the macular retinopathy or the choroidal neovascularization. The segmentation methods for these three diseases are divided into three main categories: comparative by subtraction of images, comparative with classification and saliency detection. The main challenges to characterize the leakage are the difference in luminosity between images of the angiography sequence, the similar pixel intensities of the leaks and the vessels, and the late staining of the optic disc. The comparative methods by subtraction of images are used for the automatic characterization of the leakage of central serous chorioretinopathy in angiographic sequences because they use temporal information. As the leakage area grows during examination, temporal information helps with identification. Furthermore, algorithm steps were introduced to reduce the influence of anatomical elements (such as the background, vessels, and optic disc) in localization and segmentation of leaks. The steps are frame selection, image denoising, image registration, vessel segmentation, candidate selection in early frames, vessel inpainting, IV optic disc detection, intensity normalization, background removal, image subtraction and leakage segmentation. The leakage segmentation is processed in three phases. First, candidates are selected in the subtraction image with the Otsu method. Second, the region growing algorithm is applied to the candidate regions to segment leaks in the last frame of the sequence. Finally, the segmentation of the leaks in the remaining frames of the sequence is achieved through an algorithm of active contours, i.e., in each frame, the leaks are segmented using as input the contours of the segmented leaks in the frame immediately later. As all the leaks are segmented in all frames of the sequence, the size progression of the l...

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Section: Leakage Assessment: a Literature Reviewmentioning

confidence: 99%

Section: Image Registrationmentioning

confidence: 99%

“…A. S. Fahmy et al [27] extracted three features of each coordinate of the image along the FA sequence to classify the FA image into three types: background, occult, and classic lesions.…”

Section: Segmentation In Classification Approachesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Quantitative Assessment of Central Serous Chorioretinopathy in Angiographic Sequences of Retinal Images

Ferreira

Penas

Silva

et al. 2019

2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

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Morphological prognosis prediction of choroid neovascularization from longitudinal SD‐OCT images

et al. 2023

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BackgroundChoroid neovascularization (CNV) has no obvious symptoms in the early stage, but with its gradual expansion, leakage, rupture, and bleeding, it can cause vision loss and central scotoma. In some severe cases, it will lead to permanent visual impairment.PurposeAccurate prediction of disease progression can greatly help ophthalmologists to formulate appropriate treatment plans and prevent further deterioration of the disease. Therefore, we aim to predict the growth trend of CNV to help the attending physician judge the effectiveness of treatment.MethodsIn this paper, we develop a CNN‐based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD‐OCT) B‐scans of each subject at different time points to eliminate retinal displacements of longitudinal data. Then, considering the correlation of longitudinal data, we propose a co‐segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow‐up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder‐recurrent‐decoder framework is developed for CNV growth prediction, in which an attention‐based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations.ResultsThe registration network rigidly registers the follow‐up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state‐of‐the‐art segmentation methods, the proposed segmentation network achieves high performance with the Dice similarity coefficients (Dsc) of 85.27%. Based on the above experiments, the proposed growth prediction network can play a role in predicting the future CNV morphology, and the predicted CNV has a Dsc of 83.69% with the ground truth, which is significantly consistent with the actual follow‐up visit.ConclusionThe proposed registration and segmentation networks provide the possibility for growth prediction. In addition, accurately predicting the growth of CNV enables us to know the efficacy of the drug against individuals in advance, creating opportunities for formulating appropriate treatment plans.

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Data-Dependence Dual Path Network for Choroidal Neovascularization Segmentation in SD-OCT Images

Chen

et al. 2021

Lecture Notes in Computer Science

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Segmentation of Choroidal Neovascularization lesions in fluorescein angiograms using parametric modeling of the intensity variation

Cited by 5 publications

References 6 publications

Quantitative Assessment of Central Serous Chorioretinopathy in Angiographic Sequences of Retinal Images

Quantitative Assessment of Central Serous Chorioretinopathy in Angiographic Sequences of Retinal Images

Morphological prognosis prediction of choroid neovascularization from longitudinal SD‐OCT images

Data-Dependence Dual Path Network for Choroidal Neovascularization Segmentation in SD-OCT Images

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