Age-related macular degeneration (AMD) is the major cause of visual loss in people over the age of 50 years in developed countries [1,2]. The most common form of AMD is the non-exudative variant that represents 90-95% of all AMD cases [3]. The presence of macular large (greater than 125 µm) drusen is a characteristic finding of non-exudative disease and is a key risk factor for the development of more advanced stages [4]. Drusen are historically described as focal deposits of extracellular material located between the basal lamina of the retinal pigment epithelium (RPE) and the inner collagenous layer of Bruch membrane, although sub-retinal drusenoid deposits (SDD) have also been described [5-7]. The latter are more commonly referred to as “pseudodrusen” or “reticular pseudodrusen” and represent a distinct entity; however, both are significantly associated with late AMD [8].

The exact pathogenesis of drusen formation is largely unknown, but given its composition of cellular byproducts from RPE metabolism, and primary location along the basal aspect of these cells, prevailing theories point to an age-associated disruption of cellular machinery in the photoreceptor-RPE complex. Multiple genetic and environmental factors contribute to both the onset and progression of the disease. Numerous longitudinal studies have demonstrated positive correlations between estimated total drusen area and maximum drusen size with risk of progression to advanced AMD using fundus photography [9,10]. Attempts to reduce drusen disease-burden by laser photocoagulation has failed to show visual benefit, to date [11,12].

From clinical observations, drusen are dynamic in size and distribution in the posterior pole, which highlights a potentially active process in their development and maintenance [13]. The requirement for drusen in the clinical diagnosis of AMD and risk-stratification for progression (e.g. soft vs. hard, confluent vs. isolated, bilateral vs. unilateral, small vs. intermediate vs. large), highlights the importance of understanding how drusen appear in ophthalmic imaging. With slit lamp biomicroscopy of the fundus, drusen appear as yellow-white deposits deep to the retina [14], and these changes are often difficult to quantify or qualify for clinical and research purposes. Existing AMD grading systems often require human grader’s subjective manual input, which is time consuming and difficult to maintain intra or inter-rater agreement [15]. Due to the high degree of graders’ involvement, the methods are prone to inaccuracy and poor repeatability if experienced graders are not used [16]. High-resolution retinal imaging systems have therefore become prominent and powerful diagnostic tools in the field of ophthalmology. It remains unproven the clinical impact of drusen volume determination, but certainly with improved drusen imaging we may better understand the role of drusen in the pathogenesis of the disease.

Color fundus photographs have been the gold standard to assess the clinical stage of non-exudative AMD and studying the epidemiology of the disease (Figure 1) [15,17]. Among them, the Wisconsin Age-related Maculopathy Grading System, the International Classification and the Grading System for Age-related Maculopathy and Age-related Macular Degeneration and its modified version are widely used [1,15,18].

Figure 1: Color fundus photo of high-risk non-exudative agerelated macular degeneration. There are numerous large (greater than 125 micrometer) soft drusen with central confluence in the fovea. Additionally, areas of RPE pigmentation abnormalities can be noted in the areas outside the drusen confluence.

While color photos can provide an en face two-dimensional view of the retina, they can be challenging to reliably quantify and localize drusen against the varying background of macula, RPE, and choroidal pigment [15,19,20]. A qualitative approach to drusen evaluation is likely to be less sensitive to small changes and prone to errors of inter-observer variability. Such an evaluation may also miss early changes of AMD when drusen are often smaller and isolated. Other limitations include inter-patient variability in fundus pigmentation, drusen morphology and indistinct borders, media opacity, and image quality [21]. In a prospective pilot study of eyes with intermediate AMD, Jain et al. described how hyper-pigmentation in color photographs corresponded to intraretinal pigmentary migration on spectral-domain optical coherence tomography with obscuration of underlying large drusen that were not visible on color photographs [22].

Quantification of drusen size and total area covered by drusen is an important risk factor for progression. It has been proposed to use automated software and color fundus imaging with good sensitivity and specificity for detecting drusen and quantifying drusen size and area in the macular region with standard color retinal images [23]. These (semi-) automatic image analysis methods include enhancement and segmentation of drusen. Contrast alteration has been used to improve detail registration prior to software processing. Improved contrast has been investigated with edge detection, clustering-based segmentation techniques, texture edge, and intensity thresholding.

Drusen edge detection uses delta changes in absolute pixel intensity. Soft drusen detection is hindered by their ‘feathered’, indistinct edges [24]. Clustering-based drusen segmentation aggregates ‘similar’ image components in clusters [25,26]. Texture-based drusen segmentation uses wavelet analysis to highlight surface contour in multiple orientations [27]. Different image components are separated and analyzed for signal intensity alterations and a ‘texture’ map is generated from the distribution data [28]. Thresholding-based drusen segmentation uses binary image data of frequency and pixel intensity against a normative background histogram [29]. While the above automated color photographs drusen algorithms are extremely encouraging, soft drusen detection remains problematic due to the difficulty in accurately delineating the blurred margins of large drusen.

A unified, universally accepted AMD classification scheme is lacking. AMD grading systems and severity scales have been developed which are commonly based on standardized grading of color fundus photographs. At present, there is no universally-accepted precise definition of the AMD phenotype for clinical or research purposes [30]. Many classifications incorporate drusen size, character, location, number, area, and level of pigmentation. However, serial studies have shown that drusen are changing frequently in human fundi, whether they be arising de-novo and regressing [31-33]. While late changes in AMD are generally more widely agreed upon, it is the earliest stages of AMD that remain difficult to detect and classify, either by standardized grading systems with highly trained ‘readers’ or (semi-) automated algorithms of fundus photography [30]. Either way, classifications based on color photographs alone ignore important changes such as the accumulation of autofluorescent material (lipofuscin) in the RPE, the development of reticular pseudodrusen, or the loss of rod photoreceptors [30]. Groups have shown that “pseudo-drusen” in the sub-retinal space appear remarkably similar in fundus photography to sub-RPE drusen [34]. Subretinal drusenoid deposits (SDD) are polymorphous light-grey interconnected accumulations above the RPE. Both cuticular and soft drusen appear yellow due to the removal of shorter wavelength light by a double pass through the RPE whereas SDD (which are located above the RPE) are not subjected to short wavelength attenuation and therefore are more prominent when viewed with blue light [35]. Therefore, the presence of small satellites of atrophy and the impossibility to detect three-dimensional anatomic information of retinal abnormalities limits the use of color photographs alone in drusen detection [36].

Our understanding of drusen composition and distribution in the retina is greatly enhanced by each new funduscopic imaging modality’s ability to characterize these heterogeneous, extracellular, predominately-lipoprotein aggregates [37-39]. Hard and soft drusen, cuticular drusen and reticular pseudodrusen have different imaging characteristics. Fluorescein angiography’s (FA) ability to combine red-free fundus photography and autofluorescence with additional dynamic information on the choroidal and retinal vasculature status (e.g., fluorescein staining of drusen or drusenoid pigment epithelial detachments, and “starry-sky” fundus) has aided understanding of the disease [40-41].

Soft drusen in autofluorescence imaging show hyper-autofluorescence of the borders compared to the center, which is more apparent with camera-based images than with scanning laser ophthalmoscope (SLO) images [42]. During FA, soft drusen can exhibit variable, late hyperfluorescence. Cuticular drusen are smaller in diameter, normally 50-75 microns, and are easily recognized in FA by their tight funduscopic grouping as circular punctate yellow lesions that exhibit central hypo-autofluorescence and central hyper-fluorescence in FA (Figure 2). This has been shown to be due to the RPE attenuation at the apices of cuticular drusen, a unique finding in comparison to hard drusen and reticular pseudodrusen [43,44]. The diminished concentration of cytosolic RPE melanin and lipofuscin at the center of cuticular drusen allow for greater fluorescence transmission and a decreased autofluorescence signal. Reticular pseudo-drusen is harder to identify with FA showing no change to minimal hypofluorescence [45].

Figure 2: Cuticular drusen. IR: Red Free; AF: Autofluorescence; FA: Fluorescein Angiography The cuticular drusen detailed are somewhat lost in the IR images, but highlighted by the hypo-autofluorescent centers in AF images and hyper-fluorescent centers in FA images.

IR: Red Free; AF: Autofluorescence; FA: Fluorescein Angiography

The cuticular drusen detailed are somewhat lost in the IR images, but highlighted by the hypo-autofluorescent centers in AF images and hyper-fluorescent centers in FA images.

Spectral-domain optical coherence tomography (SD-OCT) is a non-invasive, high-resolution imaging modality capable of producing high-speed three-dimensional cross-sectional images covering the central macula (Figure 3). It provides both qualitative information, such as ultrastructural changes during follow-up [46], and quantitative parameters, such as area and volume of the studied lesions. It also allows for cross-sectional visualization that characterizes microstructural alterations in the different laminae of the retina [47]. While OCT is a useful tool for the visualization of drusen, an objective assessment of drusen is limited by the lack of a method to robustly quantify these lesions on serial OCT images. Macular volume scans are also non-continuous over the macula area depending on which scanning protocol is used (e.g. five line raster versus radial scans). With automated fundus registration, serial scans illuminate the same portion of the macula with good reproducibility and improved assessment of pathology over time. Despite software advancements and image registration, a large portion of the macula is never visualized. Serial macula scan’s ‘reproducibility’ on some OCT machines can create a limited effective clinical-map ‘volume’.

Figure 3: Optical coherence tomography (OCT) imaging of nonexudative age-related macular degeneration exemplifying automated output measures such as macular volume and central macular thickness measurements. While OCT imaging with automated fundus registration allows for serial reproducible imaging of the same area of the macula, there is no accessible algorithm that tracks drusen changes over time.

It has already been shown by numerous articles that drusen position and composition change with time [31-33]. If a large portion of the macula is skipped with automated registration, drusen volume determination is at best an estimation. Automatic drusen segmentation methods have been proposed to help solve this problem. The RPE and Bruch’s membrane can be automatically delineated in SD-OCT images allowing quantification of the elevated RPE segment [48,49].

This technology must be coupled with an improved OCT-determined effective drusen volume (EDV) that more accurately reflects the actual drusen burden. While limiting inter-scan distances to drusen borders may be helpful, performing additional raster scans of the macula is time-consuming and may degrade image quality and accuracy. Eye tracking may counteract this to increase the quality scans [50,51]. The Spectralis HRA2, for example, allows for foveal centration during the longer exam times.

OCT scanning density has been studied in SD-OCT for choroidal volume measurements and an inter-scan distance of 480 µm was determined ‘clinically relevant and reliable’ [52]. Macula volume OCT reproducibility has been studied between time-domain OCT and newer SD-OCT [53]. When faster OCT (SD-OCT) technologies with greater radial scan density were used, errors of measurements were reduced. If reliable drusen thickness and volume measurements were possible with fewer scans, the time required for the raster scans and the effect of eye movements could be reduced. No study to date has determined the impact of reducing the B-scan density on drusen volume determination.

Measurement of drusen on SD-OCT may become an important imaging biomarker to enable sensitive assessment for change in disease status, response to treatment, or progression. Individual drusen are not routinely measured on SD-OCT because doing this manually is time-consuming. In addition, there are different types of drusen, some more or less amenable to automated segmentation. Khanifar et al categorized drusen ultrastructure using SD-OCT according to four morphologic parameters: shape, predominant internal reflectivity, homogeneity, and presence of overlying hyper-reflective foci [54].

Reticular pseudodrusen are found in the fundus of some patients with AMD (Figure 4). They are associated with subretinal drusenoid deposits, which are described as material that lies above the RPE [8]. This is in contrast to traditional drusen that are below the RPE, a distinction that wasn’t made definitely possible until OCT. Zweifel et al. [8] suggested that the hyper-reflective material could be graded by thickness of the accumulation above the RPE using OCT and proposed a defined grading system of three stages. According to their grading system, stage 1 is characterized by diffuse deposition of granular hyper-reflective material between the RPE and the boundary between the inner and outer segments (IS-OS) of the photoreceptors. In stage 2, mounds of accumulated materials are sufficient to alter the contour of the IS-OS boundary. And lastly, in stage 3, the material is thicker, adopts a conical appearance, and breaks through the IS-OS boundary.

Figure 4: Pseudo-drusen are best distinguished from standard drusen via optical coherence tomography. Whereas cuticular drusen and soft drusen appear as round punctate accumulations under the retinal pigment epithelium (RPE), pseudo-drusen (or subretinal drusenoid deposits) are interconnected accumulations above the RPE.

Good reproducibility has been shown by three-dimensional SD-OCT in non-exudative AMD [55]. Systems combining SD-OCT with a confocal scanning laser ophthalmoscope (cSLO) allow simultaneous recordings of cross-sectional OCT images with various topographic imaging modes. Additionally, real-time eye-tracking technology in SD-OCT systems allows for precise orientation of the scan toward the region of interest, as well as re-scanning of the region at follow-up [56].

The use of infrared imaging could be advantageous for drusen detection (Figure 5). Infrared light has a deeper penetration with minimal scatter due to media opacities (e.g., cataracts, asteroid hyalosis, syneresis) [57]. There are newer capture systems with aperture-modulation to detect scattered light off the fundus, itself [58]. Elevated structures, such as macular drusen, have a characteristic ‘moon surface’ pattern that makes their edges more visible [59].

Figure 5: Infrared image of large high-risk drusen.

Infrared scanning laser ophthalmoscopy appears to have greater sensitivity for drusen detection than conventional color fundus photographs [60,61]. Additional studies are required to determine the clinical impact, but drusen topographic maps are limited by the sensitivity of drusen capture.

If the aperture is displaced laterally from the confocal light path, a ‘pseudo-three-dimensional image’ is possible, allowing detection of drusen as raised structures with more clearly defined boundaries. This is believed to improve the contrast and sensitivity for identifying drusen, which may otherwise be difficult to distinguish in the context of a diffusely thickened Bruch membrane [62]. Unfortunately, robust automated segmentation algorithms are needed to use this data for quantitative purposes. As more powerful software becomes available, detailed drusen classification (maps and volumes) may become a possibility to assist clinical studies and assessments.

Fundus autofluorescence (FAF) using a fundus camera or a cSLO has been extensively studied (Figure 6). FAF generated with short-wavelength excitation is dominated by RPE lipofuscin [63], a complex mixture of visual fluorophores [64] that accumulates in the RPE after photoreceptor outer segment phagocytosis. Lipofuscin is a major constituent of drusen and FAF has been investigated to see if sub-clinical drusen were evident by autofluorescence before fundus photography [65-67].

Figure 6: Autofluorescence image of non-exudative age-related macular degeneration. Characteristic changes in autofluorescence imaging at the boundary of drusen can be used to define morphology and determine prognostic significance when assessing various forms of drusen.

Large soft foveal drusen often correspond to areas of increased FAF; however, the correlation between the distribution of drusen and AF is otherwise weak [68]. Other limitations to the use of FAF are the difficulty of detecting geographic atrophy (GA) and its boundaries in the presence of advanced cataracts. Also, when using the cSLO-based imaging system, it is sometimes difficult to identify the boundaries of GA in close proximity to the center of the macula because of the retinal xanthophylls that absorb the excitation light and block FAF from the underlying RPE [69].

in vivo imaging can provide high resolution images of the macular photoreceptor mosaic in patients with AMD. However, achieving detailed images of cellular structures within the retina using conventional ophthalmic techniques is limited by optical aberrations produced by ocular tissue, primarily the cornea and lens. Many of these aberrations can be overcome using emerging adaptive optics (AO) retinal imaging techniques to generate an improved imaging of retinal morphology (Figure 7).

Figure 7: AO-OCT image of a microscopic druse in a patient with age-related macular degeneration. (Reproduced With Permission from Zawadzki RJ et al. [70])

AO imaging systems traditionally consist of three principal components which actively measure and correct optical aberrations to achieve heightened resolution. The three primary components of traditional AO systems include: a wavefront sensor, a corrective optical element, and a feedback control system [71]. The wavefront sensor uses a lenslet array and a charged-couple device (CCD) camera to continuously measure distortions in the intensity and shape of the wavefront. The corrective optical element, generally a deformable mirror, compensates for optical aberrations by modifying the shape of the reflective surface. Finally, a software control system is used as an interface between the wavefront sensor and the corrective optical element. These three components work in unison to allow AO imaging systems to correct optical aberrations for enhanced image acquisition.

Since the first demonstration of AO enhanced retinal imaging nearly 15 years ago [72], these techniques have been successfully incorporated into a variety of retinal imaging systems, including fundus camera, SLO [73-75] and OCT [76,77]. Recently, multiple retinal imaging techniques have been combined with AO [70,78]. Adaptive optics-scanning laser ophthalmoscope (AO-SLO) imaging demonstrates slight disruption in the photoreceptor mosaic in early stages of AMD due to focal drusen formation and identified several small drusen deposits that are not observed with standard clinical imaging techniques [79]. Using this technology, an increase in photoreceptor disruption has been visualized within the macula in direct correlation with the stage of AMD progression leading to a decrease in visual acuity [79].

AO retinal imaging systems are currently limited to investigational use but show promise as an enhanced diagnostic imaging technique. Many studies have characterized the distribution of photoreceptors associated with aging and AMD based on histopathology. Recently, AO imaging has been used to assess the variation in cone photoreceptor density in healthy populations based on parameters such as age, eccentricity, eye length, and refractive error [80-83]. By providing cellular resolution of retinal pathology in vivo, AO enhanced imaging may help guide future treatment strategies for numerous retinal diseases such as AMD.

Hyperspectral retinal imaging (HRI) combines different images within the electromagnetic spectrum (EMS) to form an image ‘cube’ set which encompass a large range of wavelengths. HRI utilizes the visible spectrum and acquires images in three different ways: (1) sequential spatial scanning, (2) stacking of spectral bands, (3) using a “snapshot” by capturing all the spectral and spatial information simultaneously. Spatially or spectrally scanning affords the best resolution at the cost of motion artifact as the data is acquired over time. Recent work has been focused at improving “snapshot” technologies to make use of the lack of motion artifact [84]. By combining multiple wavelengths, HRI has the potential to identify spectral coefficients for macular pigments, drusen subtypes, and (deoxy-) hemoglobin [85-87]. By linking the data to a fundus photograph, the spectral data can have succinct spatial resolution. The technology has great potential to explore large areas of wavelength in white light which are currently underutilized in ophthalmology. Perhaps composition or positional information of drusen remains to be ‘translated’ from different spectral coefficient relationships.

Drusen represent the hallmark of non-exudative AMD and can exist as sub-RPE cuticular or soft drusen and as subretinal drusenoid deposits above the RPE known as pseudo-drusen. With advancing imaging techniques, we may be able to better correlate prognosis and/or response to treatment based on changes in drusen volume. Commercially available fundus photography, fluorescein angiography, infrared imaging, and optical coherence tomography can all aid in monitoring drusen with exceptional level of detail. Perhaps less widely used are autofluorescence, adaptive optics, and hyperspectral imaging; however, these techniques will continue to advance our understanding of the drusen and their role in AMD progression. Rapidly changing software algorithms will also assist in making this process more accurate, reproducible, and perhaps automated.