Automatic Change Detection of Retinal Images

Troglio, Giulia; Nappo, A.; Benediktsson, Jón Atli; Moser, Gabriele; Stefánsson, Einar

doi:10.1007/978-3-642-03891-4_75

Cited by 6 publications

(9 citation statements)

References 12 publications

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“…Troglio, Napo et al [11,12] No Pairs K&I Thresholding ARMD Marrugo et al [6] No Pairs Image correction ARMD Köse et al [5] semi Individual Raw segmentation ARMD Ramsey et al [4] No Individual Fuzzy C-Means ARMD Hussain et al [13] Yes Individual U-Nets ARMD Burlina et al [14,15] Yes Individual pre-trained CNN ARMD Kanezaki et al [24] No Individual CNN Image Processing Sublime et al [26] No…”

Section: Authors Supervised Images Used Algorithm Applicationmentioning

confidence: 99%

“…The following works are most related to our proposed algorithm as they are unsupervised algorithms applied to various eye disease images, including ARMD: In [ 11 ], Troglio et al published an improvement of their previous works realized with Nappo [ 12 ] where they use the Kittler and Illingworth (K&I) thresholding method. Their method consists of applying the K&I algorithm on random sub-images of the difference image obtained between two consecutive eye fundus images of a patient with retinopathy.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection

et al. 2020

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Age-Related Macular Degeneration (ARMD) is a progressive eye disease that slowly causes patients to go blind. For several years now, it has been an important research field to try to understand how the disease progresses and find effective medical treatments. Researchers have been mostly interested in studying the evolution of the lesions using different techniques ranging from manual annotation to mathematical models of the disease. However, artificial intelligence for ARMD image analysis has become one of the main research focuses to study the progression of the disease, as accurate manual annotation of its evolution has proved difficult using traditional methods even for experienced practicians. In this paper, we propose a deep learning architecture that can detect changes in the eye fundus images and assess the progression of the disease. Our method is based on joint autoencoders and is fully unsupervised. Our algorithm has been applied to pairs of images from different eye fundus images time series of 24 ARMD patients. Our method has been shown to be quite effective when compared with other methods from the literature, including non-neural network based algorithms that still are the current standard to follow the disease progression and change detection methods from other fields.

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Section: Authors Supervised Images Used Algorithm Applicationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection

et al. 2020

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“…The following works are most related to our proposed algorithm as they are unsupervised algorithm applied to various eye disease images, including ARMD: In [18], Troglio et al published an improvement of their previous works realized with Nappo [19] where they use the Kittler and Illingworth (K&I) thresholding method. Their method consists of applying the K&I algorithm on random sub-images of the difference image obtained between two consecutive eye fundus images of a patient with retinopathy.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Change Detection Using Joint Autoencoders for Age-Related Macular Degeneration Progression

Dupont

Kalinicheva

Sublime

et al. 2020

Artificial Neural Networks and Machine Learning – ICANN 2020

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Age-Related Macular Degeneration (ARMD) is an eye disease that has been an important research field for two decades now. Researchers have been mostly interested in studying the evolution of lesions that slowly causes patients to go blind. Many techniques ranging from manual annotation to mathematical models of the disease evolution bring interesting leads to explore. However, artificial intelligence for ARMD image analysis has become one of the main research focus to study the progression of the disease, as accurate manual annotation of its evolution has proved difficult using traditional methods even for experienced doctors. Within this context, in this paper, we propose a neural network architecture for change detection in eye fundus images to highlight the evolution of the disease. The proposed method is fully unsupervised, and is based on fully convolutional joint autoencoders. Our algorithm has been applied to several pairs of images from eye fundus images time series of ARMD patients, and has shown to be more effective than most state-of-the-art change detection methods, including non-neural network based algorithms that are usually used to follow the evolution of the disease.

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“…To achieve a comprehensive description of retinal morphology and disease progression, diverse retinal images acquired by different or the same modalities at different time instants must be registered. Retinal registration can be divided into three categories: 1) fundus-fundus registration, which is useful to expand the effective field of view and analyze changes over time [11]; 2) fundus-OCT registration, which is a registration of 2D fundus image with 3D OCT image. It requires that the 3D OCT image be reduced to 2D image by z-direction projection [12].…”

Section: Introductionmentioning

confidence: 99%

Segmentation Guided Registration for 3D Spectral-Domain Optical Coherence Tomography Images

Pan

Chen

2019

IEEE Access

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Medical image registration can be used for combining information from multiple imaging modalities, monitoring changes in size, shape or image intensity over time intervals. However, the development of such technique can be challenging for 3D spectral-domain optical coherence tomography (SD-OCT) imaging, because SD-OCT image is inherently noisy and its high resolution leads to high complexity of non-rigid registration. In this paper, a new segmentation guided approach is reported for registration of retinal OCT data. The proposed method models the 3D registration as a two-stage registration including x-y direction registration and z direction registration. In x-y direction registration, the vessel maps of OCT projection images between the template and the subject are registered to find out x-y direction displacement. The multi-scale vessel enhancement filter and morphological thinning methods are used to extract the vessel maps from the projection image of 3D OCT scans. And then x-y direction displacement is estimated by matching Speeded-Up Robust Features of the vessel maps. In z direction registration, using the tissue map instead of the original intensity image, A-scans are aligned to get the local displacements in z direction. The proposed method was evaluated on 45 longitudinal retinal OCT scans from 15 subjects. Experimental results show that the proposed method is accurate and very efficient.

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Automatic Change Detection of Retinal Images

Cited by 6 publications

References 12 publications

Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection

Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection

Unsupervised Change Detection Using Joint Autoencoders for Age-Related Macular Degeneration Progression

Segmentation Guided Registration for 3D Spectral-Domain Optical Coherence Tomography Images

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