Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image analysis tasks, the majority of training examples are easy to classify and therefore contribute little to the CNN learning process. In this paper, we propose a method to improve and speed-up the CNN training for medical image analysis tasks by dynamically selecting misclassified negative samples during training. Training samples are heuristically sampled based on classification by the current status of the CNN. Weights are assigned to the training samples and informative samples are more likely to be included in the next CNN training iteration. We evaluated and compared our proposed method by training a CNN with (SeS) and without (NSeS) the selective sampling method. We focus on the detection of hemorrhages in color fundus images. A decreased training time from 170 epochs to 60 epochs with an increased performance-on par with two human experts-was achieved with areas under the receiver operating characteristics curve of 0.894 and 0.972 on two data sets. The SeS CNN statistically outperformed the NSeS CNN on an independent test set.

PURPOSE. Abnormal choroidal blood flow is considered important in the pathogenesis of chronic central serous chorioretinopathy (CSC). Optical coherence tomography (OCT) angiography can image ocular blood cell flow and could thus provide novel insights in disease mechanisms of CSC. We evaluated depth-resolved flow in chronic CSC by OCT angiography compared to fluorescein angiography (FA) and indocyanine green angiography (ICGA).METHODS. Eighteen eyes with chronic CSC, and six healthy controls, were included. Two human observers annotated areas of staining, hypofluorescence, and hotspots on FA and ICGA, and areas of abnormal flow on OCT angiography. Interobserver agreement in annotating OCT angiography and FA/ICGA was measured by Jaccard indices (JIs). We assessed colocation of flow abnormalities and subretinal fluid visible on OCT, and the distance between hotspots on ICGA from flow abnormalities.RESULTS. Abnormal areas were most frequently annotated in late-phase ICGA and choriocapillary OCT angiography, with moderately high (median JI, 0.74) and moderate (median JI, 0.52) interobserver agreement, respectively. Abnormalities on late-phase ICGA and FA colocated with those on OCT angiography. Aberrant choriocapillary OCT angiography presented as foci of reduced flow surrounded by hyperperfused areas. Hotspots on ICGA were located near hypoperfused spots on OCT angiography (mean distance, 168 lm). Areas with current or former subretinal fluid were colocated with flow abnormalities.CONCLUSIONS. On OCT angiography, chronic CSC showed irregular choriocapillary flow patterns, corresponding to ICGA abnormalities. These results suggest focal choriocapillary ischemia with surrounding hyperperfusion that may lead to subretinal fluid leakage.

We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms. The algorithm accurately estimated macular thickness with an error of 14.0 ± 22.1 µm, substantially lower than the error obtained using the other algorithms (42.9 ± 116.0 µm and 27.1 ± 69.3 µm, respectively). These results highlighted the proposed algorithm's capability of modeling the wide variability in retinal appearance and obtained a robust and reliable retina segmentation even in severe pathological cases.

Age-related Macular Degeneration (AMD) is a common eye disorder with high prevalence in elderly people. The disease mainly affects the central part of the retina, and could ultimately lead to permanent vision loss. Optical Coherence Tomography (OCT) is becoming the standard imaging modality in diagnosis of AMD and the assessment of its progression. However, the evaluation of the obtained volumetric scan is time consuming, expensive and the signs of early AMD are easy to miss. In this paper we propose a classification method to automatically distinguish AMD patients from healthy subjects with high accuracy. The method is based on an unsupervised feature learning approach, and processes the complete image without the need for an accurate pre-segmentation of the retina. The method can be divided in two steps: an unsupervised clustering stage that extracts a set of small descriptive image patches from the training data, and a supervised training stage that uses these patches to create a patch occurrence histogram for every image on which a random forest classifier is trained. Experiments using 384 volume scans show that the proposed method is capable of identifying AMD patients with high accuracy, obtaining an area under the Receiver Operating Curve of 0.984. Our method allows for a quick and reliable assessment of the presence of AMD pathology in OCT volume scans without the need for accurate layer segmentation algorithms.

A machine learning system capable of automatically grading OCT scans into AMD severity stages was developed and showed similar performance as human observers. The proposed automatic system allows for a quick and reliable grading of large quantities of OCT scans, which could increase the efficiency of large-scale AMD studies and pave the way for AMD screening using OCT.

Citation: van Grinsven MJJP, Lechanteur YTE, van de Ven JPH, et al. Automatic drusen quantification and risk assessment of age-related macular degeneration on color fundus images. Invest Ophthalmol Vis Sci. 2013;54:3019-3027. DOI:10.1167/ iovs.12-11449 PURPOSE. To evaluate a machine learning algorithm that allows for computer-aided diagnosis (CAD) of nonadvanced age-related macular degeneration (AMD) by providing an accurate detection and quantification of drusen location, area, and size.METHODS. Color fundus photographs of 407 eyes without AMD or with early to moderate AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically detect and quantify drusen on each image. Based on detected drusen, the CAD software provided a risk assessment to develop advanced AMD. Evaluation of the CAD system was performed using annotations made by two blinded human graders.RESULTS. Free-response receiver operating characteristics (FROC) analysis showed that the proposed system approaches the performance of human observers in detecting drusen. The estimated drusen area showed excellent agreement with both observers, with mean intraclass correlation coefficients (ICC) larger than 0.85. Maximum druse diameter agreement was lower, with a maximum ICC of 0.69, but comparable to the interobserver agreement (ICC ¼ 0.79). For automatic AMD risk assessment, the system achieved areas under the receiver operating characteristic (ROC) curve of 0.948 and 0.954, reaching similar performance as human observers. CONCLUSIONS.A machine learning system capable of separating high-risk from low-risk patients with nonadvanced AMD by providing accurate detection and quantification of drusen, was developed. The proposed method allows for quick and reliable diagnosis of AMD, opening the way for large dataset analysis within population studies and genotype-phenotype correlation analysis.Keywords: age-related macular degeneration, drusen detection, risk assessment A ge-related macular degeneration (AMD) is the leading cause of irreversible vision loss in developed countries among individuals older than 50 years.1 AMD is a gradual progressive disease that evolves from early and intermediate stages, with no or subtle visual changes, to an advanced stage, where the loss of central vision can occur. Patients with intermediate AMD are at higher risk of developing advanced AMD and thus suffering from severe visual loss, and they should undergo routine-and self-monitoring for a timely diagnosis.2 Lifestyle changes such as cessation of smoking and prophylactic regimen like vitamin supplementation are recommended for patients at risk in order to slow progression of the disease. 3-6Deposits of extracellular material localized between the inner collagenous layer of Bruch's membrane and the basal lamina of the RPE, known as drusen, are considered the hallmark feature of AMD.7 Macular drusen are important in the context of AMD grading, and certain drusen characteristics are associated with progres...

Observer performance and agreement for RPD identification improved significantly by using a multimodality grading approach. The developed automatic system showed similar performance as observers, and automatic RPD area quantification was in concordance with manual delineations. The proposed automatic system allows for a fast and accurate identification and quantification of RPD, opening the way for efficient quantitative imaging biomarkers in large data set analysis.

PurposeAge-related macular degeneration is a common form of vision loss affecting older adults. The etiology of AMD is multifactorial and is influenced by environmental and genetic risk factors. In this study, we examine how 19 common risk variants contribute to drusen progression, a hallmark of AMD pathogenesis.MethodsExome chip data was made available through the International AMD Genomics Consortium (IAMDGC). Drusen quantification was carried out with color fundus photographs using an automated drusen detection and quantification algorithm. A genetic risk score (GRS) was calculated per subject by summing risk allele counts at 19 common genetic risk variants weighted by their respective effect sizes. Pathway analysis of drusen progression was carried out with the software package Pathway Analysis by Randomization Incorporating Structure.ResultsWe observed significant correlation with drusen baseline area and the GRS in the age-related eye disease study (AREDS) dataset (ρ = 0.175, P = 0.006). Measures of association were not statistically significant between drusen progression and the GRS (P = 0.54). Pathway analysis revealed the cell adhesion molecules pathway as the most highly significant pathway associated with drusen progression (corrected P = 0.02).ConclusionsIn this study, we explored the potential influence of known common AMD genetic risk factors on drusen progression. Our results from the GRS analysis showed association of increasing genetic burden (from 19 AMD associated loci) to baseline drusen load but not drusen progression in the AREDS dataset while pathway analysis suggests additional genetic contributors to AMD risk.