Meindert Niemeijer

• University of Iowa

In medical image analysis applications, the availability of large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting,...

Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from data. For that, we pair true eye...

Purpose to compare performance of a deep learning enhanced algorithm for automated detection of diabetic retinopathy, to the previously published performance of that algorithm, the Iowa Detection Program (IDP) - without deep learning components - on the same publicly available set of fundus images and previously reported consensus reference standa...

Automated methods for mass screening of diabetic retinopathy can potentially address the limited sensitivity, higher cost, longer turnaround times, and low reproducibility of human readers. This chapter introduces the typical computer algorithms and imaging protocols that have been used to test and validate automated screening, as well as the diabe...

Purpose: To evaluate the intersession repeatability of retinal thickness measurements in patients with diabetic macular edema (DME) using the Heidelberg Spectralis optical coherence tomography (OCT) algorithm and a publicly available, three-dimensional graph search-based multilayer OCT segmentation algorithm, the Iowa Reference Algorithm. Methods...

Importance The diagnostic accuracy of computer detection programs has been reported to be comparable to that of specialists and expert readers, but no computer detection programs have been validated in an independent cohort using an internationally recognized diabetic retinopathy (DR) standard. Objective To determine the sensitivity and specificity...

Glaucoma is one of the major causes of blindness worldwide. One important structural parameter for the diagnosis and management of glaucoma is the cup-to-disc ratio (CDR), which tends to become larger as glaucoma progresses. While approaches exist for segmenting the optic disc and cup within fundus photographs, and more recently, within spectral-do...

In ophthalmology, various modalities and tests are utilized to obtain vital information on the eye's structure and function. For example, optical coherence tomography (OCT) is utilized to diagnose, screen, and aid treatment of eye diseases like macular degeneration or glaucoma. Such data are complemented by photographic retinal fundus images and fu...

A novel splat feature classification method is presented with application to retinal hemorrhage detection in fundus images. Reliable detection of retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. Under our supervised approach, retinal color images are partitioned into non-overl...

Purpose: Disruption of external limiting membrane (ELM) integrity on spectral-domain optical coherence tomography (SD-OCT) is associated with lower visual acuity outcomes in patients suffering from diabetic macular edema (DME). However, no automated methods to detect ELM and/or determine its integrity from SD-OCT exist. Methods: Sixteen subjects...

Purpose: We developed and evaluated a fully automated 3-dimensional (3D) method for segmentation of the choroidal vessels, and quantification of choroidal vasculature thickness and choriocapillaris-equivalent thickness of the macula, and evaluated repeat variability in normal subjects using standard clinically available spectral domain optical coh...

The calcium burden as estimated from non-ECGsynchronized CT exams acquired in screening of heavy smokers has been shown to be a strong predictor of cardiovascular events. We present a method for automatic coronary calcium scoring with low-dose, non-contrast-enhanced, non-ECG-synchronized chest CT. First, a probabilistic coronary calcium map was cre...

Objectives To test the hypothesis that the amount and distribution of glaucomatous damage along the entire retinal ganglion cell–axonal complex (RGC-AC) can be quantified and to map the RGC-AC connectivity in early glaucoma using automated image analysis of standard spectral-domain optical coherence tomography. Methods Spectral-domain optical coher...

Segmenting retinal vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes is particularly challenging due to the projected neural canal opening (NCO) and relatively low visibility in the ONH center. Color fundus photographs provide a relatively high vessel contrast in the region inside the NCO, but...

An automated method is reported for segmenting 3-D fluid-associated abnormalities in the retina, so-called symptomatic exudate-associated derangements (SEAD), from 3-D OCT retinal images of subjects suffering from exudative age-related macular degeneration. In the first stage of a two-stage approach, retinal layers are segmented, candidate SEAD reg...

The introduction of spectral Optical Coherence Tomography (OCT) scanners has enabled acquisition of high resolution, 3D cross-sectional volumetric images of the retina. 3D-OCT is used to detect and manage eye diseases such as glaucoma and age-related macular degeneration. To follow-up patients over time, image registration is a vital tool to enable...

To correlate the thicknesses of focal regions of the macular ganglion cell layer with those of the peripapillary nerve fiber layer using spectral-domain optical coherence tomography (SD-OCT) in glaucoma subjects. Macula and optic nerve head SD-OCT volumes were obtained in 57 eyes of 57 subjects with open-angle glaucoma or glaucoma suspicion. Using...

This paper proposes an algorithm to measure the width of retinal vessels in fundus photographs using graph-based algorithm to segment both vessel edges simultaneously. First, the simultaneous two-boundary segmentation problem is modeled as a two-slice, 3-D surface segmentation problem, which is further converted into the problem of computing a mini...

A decreased ratio of the width of retinal arteries to veins [arteriolar-to-venular diameter ratio (AVR)], is well established as predictive of cerebral atrophy, stroke and other cardiovascular events in adults. Tortuous and dilated arteries and veins, as well as decreased AVR are also markers for plus disease in retinopathy of prematurity. This wor...

Contextual information plays an important role in medical image understanding. Medical experts make use of context to detect and differentiate pathologies in medical images, especially when interpreting difficult cases. The majority of computer-aided diagnosis (CAD) systems, however, employ only local information to classify candidates, without tak...

To evaluate the performance of a comprehensive computer-aided diagnosis (CAD) system for diabetic retinopathy (DR) screening, using a publicly available database of retinal images, and to compare its performance with that of human experts. A previously developed, comprehensive DR CAD system was applied to 1200 digital color fundus photographs (nonm...

Segmenting vessels in spectral-domain optical coherence tomography (SD-OCT) volumes is particularly challenging in the region near and inside the neural canal opening (NCO). Furthermore, accurately segmenting them in color fundus photographs also presents a challenge near the projected NCO. However, both modalities also provide complementary inform...

Reliable detection of large retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. In this study, we propose a novel large retinal hemorrhages detection method based on splat feature classification. Fundus photographs are partitioned into a number of splats covering the entire image...

A reliable and accurate method to measure the width of retinal blood vessel in fundus photography is proposed in this paper. Our approach is based on a graph-theoretic algorithm. The two boundaries of the same blood vessel are segmented simultaneously by converting the two-boundary segmentation problem into a two-slice, threedimension surface segme...

Diabetic Retinopathy (DR) is a vascular disorder affecting the retina due to prolonged Diabetes. It can lead to sudden vision loss in advanced stages. Screening and routine monitoring is the most effective way of avoiding vision loss due to DR. Abramoff et al.[1] developed and evaluated an automated DR screening system. One of the most important pa...

Parameters extracted from the vasculature on the retina are correlated with various conditions such as diabetic retinopathy and cardiovascular diseases such as stroke. Segmentation of the vasculature on the retina has been a topic that has received much attention in the literature over the past decade. Analysis of the segmentation result, however,...

Computer-aided detection (CAD) is increasingly used in clinical practice and for many applications a multitude of CAD systems have been developed. In practice, CAD systems have different strengths and weaknesses and it is therefore interesting to consider their combination. In this paper, we present generic methods to combine multiple CAD systems a...

This paper proposes an algorithm to measure the width of retinal vessels in fundus photographs using graph-based algorithm to segment both vessel edges simultaneously. First, the simultaneous two-boundary segmentation problem is modeled as a two-slice, three-dimension surface segmentation problem, which is further converted into the problem of comp...

Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lun...

We present a method for automatically segmenting the blood vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes, with a focus on the ability to segment the vessels in the region near the neural canal opening (NCO). The algorithm first pre-segments the NCO using a graph-theoretic approach. Oriented...

To compare the performance of automated diabetic retinopathy (DR) detection, using the algorithm that won the 2009 Retinopathy Online Challenge Competition in 2009, the Challenge2009, against that of the one currently used in EyeCheck, a large computer-aided early DR detection project. Evaluation of diagnostic test or technology. Fundus photographi...

Optical coherence tomography (OCT), being a noninvasive imaging modality, has begun to find vast use in the diagnosis and management of ocular diseases such as glaucoma, where the retinal nerve fiber layer (RNFL) has been known to thin. Furthermore, the recent availability of the considerably larger volumetric data with spectral-domain OCT has incr...

Automated identification of diabetic retinopathy (DR), the primary cause of blindness and visual loss for those aged 18-65 years, from color images of the retina has enormous potential to increase the quality, cost-effectiveness and accessibility of preventative care for people with diabetes. Through advanced image analysis techniques, retinal imag...

Segmentation of retinal blood vessels can provide important information for detecting and tracking retinal vascular diseases including diabetic retinopathy, arterial hypertension, arteriosclerosis and retinopathy of prematurity (ROP). Many studies on 2-D segmentation of retinal blood vessels from a variety of medical images have been performed. How...

A lower ratio between the width of the arteries and veins (Arteriolar-to-Venular diameter Ratio, AVR) on the retina, is well established to be predictive of stroke and other cardiovascular events in adults, as well as an increased risk of retinopathy of prematurity in premature infants. This work presents an automatic method that detects the locati...

Glaucoma is the second leading ocular disease causing blindness due to gradual damage to the optic nerve and resultant visual field loss. Segmentations of the optic disc cup and neuroretinal rim can provide important parameters for detecting and tracking this disease. The purpose of this study is to describe and evaluate a method that can automatic...

In recent years a number of retinal image databases have been made publicly available. A popular image database in the retinal image analysis field is the DRIVE retinal vasculature database. In general this type of image database contains a reference standard, in case of the DRIVE database these are segmentations of the vasculature, usually produce...

Contextual information is of paramount importance in medical image understanding to detect and differentiate pathologies, especially when interpreting difficult cases. Current computer-aided detection (CAD) systems typically employ only local information to classify candidates, without taking into account global image information or the relation of...

The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these were compared with each other on the same data. In this work we present...

The performance of computer-aided diagnosis (CAD) systems can be highly influenced by the training strategy. CAD systems are traditionally trained using available labeled data, extracted from a specific data distribution or from public databases. Due to the wide variability of medical data, these databases might not be representative enough when th...

A fully automated, fast method to detect the fovea and the optic disc in digital color photographs of the retina is presented. The method makes few assumptions about the location of both structures in the image. We define the problem of localizing structures in a retinal image as a regression problem. A kNN regressor is utilized to predict the dist...

To evaluate the performance of an automated algorithm for determination of the cup and rim from close-to-isotropic spectral domain (SD) OCT images of the optic nerve head (ONH) and compare to the cup and rim as determined by glaucoma experts from stereo color photographs of the same eye. Thirty-four consecutive patients with glaucoma were included...

The purpose of computer-aided detection or diagnosis (CAD) technology has so far been to serve as a second reader . If, however, all relevant lesions in an image can be detected by CAD algorithms, use of CAD for automatic reading or prescreening may become feasible. This work addresses the question how to fuse information from multiple CAD algorith...

The optic disc margin is of interest due to its use for detecting and managing glaucoma. We developed a method for segmenting the optic disc margin of the optic nerve head (ONH) in spectral-domain optical coherence tomography (OCT) images using a graph-theoretic approach. A small number of slices surrounding the Bruch's membrane opening (BMO) plane...

Glaucoma is a group of diseases which can cause vision loss and blindness due to gradual damage to the optic nerve. The ratio of the optic disc cup to the optic disc is an important structural indicator for assessing the presence of glaucoma. The purpose of this study is to develop and evaluate a method which can segment the optic disc cup and neur...

Retinal vessel segmentation is a prerequisite for the analysis of vessel parameters such as tortuosity, variation of the vessel width along the vessel and the ratio between the venous and arterial vessel width. This analysis can provide indicators for the presence of a wide range of diseases. Different types of approaches have been proposed to segm...

Separating the retinal vascular tree into arteries and veins is important for quantifying vessel changes that preferentially affect either the veins or the arteries. For example the ratio of arterial to venous diameter, the retinal a/v ratio, is well established to be predictive of stroke and other cardiovascular events in adults, as well as the st...

The recent introduction of next generation spectral OCT scanners has enabled routine acquisition of high resolution, 3D cross-sectional volumetric images of the retina. 3D OCT is used in the detection and management of serious eye diseases such as glaucoma and age-related macular degeneration. For follow-up studies, image registration is a vital to...

Computer-aided Diagnosis (CAD) systems for the automatic identification of abnormalities in retinal images are gaining importance in diabetic retinopathy screening programs. A huge amount of retinal images are collected during these programs and they provide a starting point for the design of machine learning algorithms. However, manual annotations...

The latest generation of spectral optical coherence tomography (OCT) scanners is able to image 3D cross-sectional volumes of the retina at a high resolution and high speed. These scans offer a detailed view of the structure of the retina. Automated segmentation of the vessels in these volumes may lead to more objective diagnosis of retinal vascular...

To evaluate the performance of a system for automated detection of diabetic retinopathy in digital retinal photographs, built from published algorithms, in a large, representative, screening population. We conducted a retrospective analysis of 10,000 consecutive patient visits, specifically exams (four retinal photographs, two left and two right) f...

The detection of the position of the normal anatomy in color fundus photographs is an important step in the automated analysis of retinal images. An automatic system for the detection of the position of the optic disc and the fovea is presented. The method integrates the use of local vessel geometry and image intensity features to find the correct...

To describe and evaluate a machine learning-based, automated system to detect exudates and cotton-wool spots in digital color fundus photographs and differentiate them from drusen, for early diagnosis of diabetic retinopathy. Three hundred retinal images from one eye of 300 patients with diabetes were selected from a diabetic retinopathy telediagno...

An automatic system is presented to find the location of the major anatomical structures in color fundus photographs; the optic disc, the macula, and the vascular arch. These structures are found by fitting a single point-distribution-model to the image, that contains points on each structure. The method can handle optic disc and macula centered im...

Reliable verification of image quality of retinal screening images is a prerequisite for the development of automatic screening systems for diabetic retinopathy. A system is presented that can automatically determine whether the quality of a retinal screening image is sufficient for automatic analysis. The system is based on the assumption that an...

The automatic detection of the position of the optic disc is an important step in the automatic analysis of retinal images. A method to detect the approximate position of the optic disc using kNN regression is presented. The method starts by building a regression model of the optic disc position. Using a prior vessel segmentation all vessel pixels...

Diabetic retinopathy is a common ocular complication of diabetes. It is the most frequent cause of blindness in the working population of the United States and the European Union. Early diagnosis, and treatment can prevent vision loss in the majority of cases. Yet only approximately 50% of people with diabetes are regularly screened for the presenc...

The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy. In this paper, a novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al. (1998) with two important n...

We present a novel method that determines whether a macula centered retinal image is from the left or right eye and automatically detects the optic disc, the fovea and the vascular arch by inferring the location of a set of landmarks placed on these structures. The algorithm relies on a specific energy function that combines global and local cues....

Conventional methods for the segmentation of lung fields from thorax CT scans are based on thresholding. They rely on a large grey value contrast between the lung parenchyma and surrounding tissues. In the presence of consolidations or other high density pathologies, these methods fail. For the segmentation of such scans, a lung shape should be ind...

In this work we compare the performance of a number of vessel segmentation algorithms on a newly constructed retinal vessel image database. Retinal vessel segmentation is important for the detection of numerous eye diseases and plays an important role in automatic retinal disease screening systems. A large number of methods for retinal vessel segme...

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used...

The skeletal maturity of children is usually assessed from a standard radiograph of the left hand and wrist. An established clinical method to determine the skeletal maturity is the Tanner-Whitehouse (TW2) method. This method divides the skeletal development into several stages (labelled A, B, ...,I). We are developing an automated system based on...

The performance of computer-aided diagnosis (CAD) systems can be highly influenced by the training strategy. CAD systems are traditionally trained using available labeled data, extracted from a specific data distribution or from public databases. Due to the wide variability of medical data, these databases might not be representative enough when th...