Segmentation of Choroidal Neovascularization in Fundus Fluorescein Angiograms

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

doi:10.1109/tbme.2013.2237906

Cited by 20 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Classical lesion detection were segmentation methods based on mathematical morphology, region growing, or pixel classification, using fundus images 18 – 20 . Walid presented a framework for segmenting Choroidal neovascularization lesions based on parametric modeling of the intensity variation in FFA 21 . However, limited expert labeled data is a common problem encountered.…”

Section: Discussionmentioning

confidence: 99%

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning

Jin

Pan

You

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,014 FFA images of 221 patients with DME. We use 3 convolutional neural networks (CNNs), including DenseNet, ResNet50, and VGG16, to identify non-perfusion regions (NP), microaneurysms, and leakages in FFA images. The NPA was segmented using attention U-net. To validate its performance, we applied our detection algorithm on 249 FFA images in which the NPA areas were manually delineated by 3 ophthalmologists. For DR lesion classification, area under the curve is 0.8855 for NP regions, 0.9782 for microaneurysms, and 0.9765 for leakage classifier. The average precision of NP region overlap ratio is 0.643. NP regions of DME in FFA images are identified based a new automated deep learning algorithm. This study is an in-depth study from computer-aided diagnosis to treatment, and will be the theoretical basis for the application of intelligent guided laser.

show abstract

Section: Discussionmentioning

confidence: 99%

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning

Jin

Pan

You

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In the proposed work, the Frangi filter is employed for the enhancement of the vascular network in the retinal image. [22][23][24][25][26][27][28] The Hessian eigenvalues-based approach is used in the Frangi filter for the contrast enhancement of foreground pixels that is, blood vessels. As a result, nonvessel pixels of the major and minor vessels are eliminated.…”

Section: Retinal Vessel Segmentation Using Frangi Filtermentioning

confidence: 99%

Classification of neovascularization on retinal images using extreme learning machine

Pappu¹,

Biswal²,

Gandhi

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Proliferative diabetic retinopathy is the advanced stage of diabetic retinopathy (DR) resulting in the growth of abnormal vessels on the retinal surface termed as neovascularization. This article primarily deals with the timely detection and classification of retinal images into healthy, neovascularization on optic disc, and elsewhere using an extreme learning machine (ELM) classifier. Initially, a binary mask is employed to enhance the foreground pixels. The resultant image is processed for the extraction of a retinal vessel map using Hessian-based Frangi filter. For the classification of retinal images, an optimal feature set of 14 features including seven-moment invariant-based features are extracted from the vascular map using sequential recursive feature elimination algorithm. Further, the training of the ELM classifier is carried out using the K-fold cross-validation technique to improve the performance of the classifier.The proposed method achieves an average accuracy of 98% and an average error rate of less than 0.005 when tested on different globally accessible datasets. Apart from the different statistical results like sensitivity, specificity, and area under curve are estimated as 98.5%, 100%, and 94.2%, respectively, for validating the algorithm. Comparison results reveal that the ELM classifier outperforms the SVM classifier in terms of accuracy and error rate.

show abstract

“…Choroidal neovascularization (CNV), also known as subretinal neovascularization, is a basic pathological change of various intraocular diseases such as age-related macular degeneration, central exudative chorioretinopathy, idiopathic choroidal neovascularization, pathological myopic macular degeneration, and ocular histoplasmosis syndrome (DeWan et al, 2006;Abdelmoula et al, 2013;Jia et al, 2014;Liu et al, 2015;Zhu et al, 2017). It often involves the macula, causing serious damage to the central vision.…”

Section: Introductionmentioning

confidence: 99%

MF-Net: Multi-Scale Information Fusion Network for CNV Segmentation in Retinal OCT Images

Meng

Wang

et al. 2021

Front. Neurosci.

View full text Add to dashboard Cite

Choroid neovascularization (CNV) is one of the blinding ophthalmologic diseases. It is mainly caused by new blood vessels growing in choroid and penetrating Bruch's membrane. Accurate segmentation of CNV is essential for ophthalmologists to analyze the condition of the patient and specify treatment plan. Although many deep learning-based methods have achieved promising results in many medical image segmentation tasks, CNV segmentation in retinal optical coherence tomography (OCT) images is still very challenging as the blur boundary of CNV, large morphological differences, speckle noise, and other similar diseases interference. In addition, the lack of pixel-level annotation data is also one of the factors that affect the further improvement of CNV segmentation accuracy. To improve the accuracy of CNV segmentation, a novel multi-scale information fusion network (MF-Net) based on U-Shape architecture is proposed for CNV segmentation in retinal OCT images. A novel multi-scale adaptive-aware deformation module (MAD) is designed and inserted into the top of the encoder path, aiming at guiding the model to focus on multi-scale deformation of the targets, and aggregates the contextual information. Meanwhile, to improve the ability of the network to learn to supplement low-level local high-resolution semantic information to high-level feature maps, a novel semantics-details aggregation module (SDA) between encoder and decoder is proposed. In addition, to leverage unlabeled data to further improve the CNV segmentation, a semi-supervised version of MF-Net is designed based on pseudo-label data augmentation strategy, which can leverage unlabeled data to further improve CNV segmentation accuracy. Finally, comprehensive experiments are conducted to validate the performance of the proposed MF-Net and SemiMF-Net. The experiment results show that both proposed MF-Net and SemiMF-Net outperforms other state-of-the-art algorithms.

show abstract

Segmentation of Choroidal Neovascularization in Fundus Fluorescein Angiograms

Cited by 20 publications

References 9 publications

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning

Classification of neovascularization on retinal images using extreme learning machine

MF-Net: Multi-Scale Information Fusion Network for CNV Segmentation in Retinal OCT Images

Contact Info

Product

Resources

About