Ординатура / Офтальмология / Английские материалы / Retinal Degenerations biology, diagnostics, and therapeutics_Tombran-Tink, Barnstable_2007

secreted by the RPE, thereby resulting in drusen deposition. Further, calcification and fracture of Bruch’s membrane may predispose to choroidal neovascularization. Here, we see that drusen may be symptomatic of vascular disease, suggesting that AMD might be slowed were hypertension and atherosclerosis better controlled.

Although theories of drusen formation, composition, and relevance abound, it is clear that drusen play a central role in the physician’s ability to diagnose AMD early, and perhaps prevent its progression. Further, these theories are not inconsistent. Thus, the modern model of atherosclerosis (20) as immune-mediated suggests a relationship between the vascular and inflammatory concepts

Therapeutic Laser

Therapeutic attempts to treat early AMD have included laser photocoagulation for drusen (21). Gass first reported that laser photocoagulation resulted in drusen resolution, and suggested such prophylactic treatment (22,23). Interestingly, the MPS investigated the role of photocoagulation in treating existing CNV, but not soft drusen in early AMD (24). Wetzig studied drusen resorption following photocoagulation (in a scatter pattern) on 77 eyes, without using controls. There was an impressive result of decreased overall drusen but the 12% progression to CNV raised concerns (25). In a small trial in 1994, Figueroa et al. established that laser photocoagulation to the peripheral macula could result in resolution of drusen without complications, not only near the treatment site, but also throughout the rest of the macula (26). In 1998, the Choroidal Neovascularization Prevention Treatment Group reported that treated eyes with early AMD had a significant rate of conversion to CNV in patients with CNV in the fellow eye (27). Most of the CNV developed in the region of laser treatment. As a result, the trial has since enrolled only patients with no preexisting CNV. Now known as the Complications of AMD Prevention Trial, it is an ongoing trial of more than 1000 patients with bilateral large drusen only. Results are expected in late 2006. The European drusen laser study also found laser contra-indicated in fellow eyes of eyes with CNV (28). Olk et al. are also evaluating subthreshold diode laser in the The Prophylactic Treatment of Non exudative AMD Trial, with early equivocal results (Friberg TR, Results of the Bilateral Arm of the PTAMD, Macula Society Meeting, February, 2006). Clearly, in the eyes of investigators, there remains hope that laser treatment may become both safe and effective in the treatment of patients with early AMD and no evidence of advanced disease.

IMAGING

Introduction to Retinal Image Analysis

Clinical medical retinal research, in particular, and visual science in humans, in general, is based on minimally invasive testing with imaging serving as the surrogate for biopsy. Given the transparency of ocular tissue, retinal images are able to provide large amounts of valuable information. Image analysis of the retina can be performed in a variety of settings ranging from the standard digital fundus photograph, to autofluorescence imaging to infrared imaging, all of which provide unique information to the viewer. Combined analysis of imaging data from multiple methods can reveal heretofore-unexpected relationships.

The power of engineering technology to image these lesions in a variety of ways has grown exponentially, as has the raw digital power to store and process these images.

Image analysis to interpret this wealth of data unfortunately lagged far behind. Current systems for analyzing fundus photographs are manual, subjective, and expensive. Efficient and quantitative analysis of these images in clinical research is therefore needed to provide high throughput, cost savings, and accurate conclusions in large-scale studies. As an estimate, image grading for a study on the scale of Age-Related Eye Disease Study (AREDS) (29), with a study population of 3640 examined every 6 mo for 6.3 yr, a total of 46,000 exams, with film photographs graded manually at $150/exam, would cost $6.9 million. The same study with digital imaging and automatic image analysis would consume a fraction of these resources.

Color Fundus Photography

As previously mentioned, extensive drusen area as seen on the fundus photograph is the greatest risk factor for the progression of AMD (1,8,30–35). Historically, the accepted standard for drusen grading in AMD was manual grading of stereo photographic pairs at the light box, for example as refined by Klein et al. in the Wisconsin grading system (3,36). However, there was always difficulty in obtaining inter-observer agreement in drusen identification. Inter-observer agreement in the presence of soft drusen only was 89% and on the total number of drusen was 76% in one study by Bressler (37). Further, examiners were asked to aggregate mentally the amount of drusen occupying a given macular subfield (3), as in the International System where drusen areas were estimated to within 10 to 25% or 25 to 50%, and so on (36). Even these semiquantitative estimates proved difficult for human observers. Clearly, there was a pressing need for techniques that allowed more precise and confident measurements of macular drusen loads, e.g., to within 5%, to improve the quality of data being gathered in clinical trials and epidemiological studies.

Autofluorescence, Drusen, Lipofuscin

In addition to standard fundus photography, autofluorescence (AF) imaging with the scanning laser ophthalmoscope (SLO) has played a greater role in understanding drusen and AMD. It is already clear that the autofluorescence of RPE lipofuscin, which contains known fluorophores including A2E, is related to AMD (38,39). In a landmark study, von Ruckmann et al. demonstrated focally increased AF (FIAF) in a broad range of AMD patients (40,41). For further details on the relationship of RPE lipofuscin to the cell biology of AMD, see the companion chapter by Sparrow. Lipofuscin is also imaged by AF as it accumulates in the flecks of juvenile macular degeneration or Stargardt disease (STGD). For greater details, see Chapter 5 (the companion chapter by Allikmets).

Recent work has dealt with the relationship between the distribution of drusen and increased or decreased AF. The subjective study of Lois et al. (40) reported that FIAF and drusen are mostly independent markers for Stage 3 AMD. However, evidence using image registration techniques subsequently pointed to a possible co-localization between drusen and autofluorescence when only large soft drusen are present, and changes in this relationship when pigment abnormalities or geographic atrophy are also present (42). See Figs. 1 and 2. Image registration allows precise comparison between fundus photographs and AF images. These techniques may prove consequential in future studies of the dynamic characteristics and life history of drusen as they correspond to lipofuscin distribution in a patient with AMD.

Fig. 1. (A) Original AF image. Image has been registered with the color photo (E). Note exact vascular correspondence, verifying good registration. The red dots show the selection of AF background, avoiding vessels and FIAF. (B) Geometric model constructed from the background points of AF image. (C) AF image leveled by the model constructed from the background dots. FIAF centrally, which was dim in the original, is now distinct. (D) FIAF segmentation by an Otsu threshold. (E) Original fundus image with drusen (3000- m region). (F) Green channel of original image. (G) Drusen segmentation by leveling/thresholding (F), superimposed in green on

(E). (H) FIAF superimposed on the drusen in the color photo, showing 78% of FIAF co-localized with drusen.

Fig. 2. (A) The original fundus photograph with drusen and GA. (B) Drusen segmented by the automated method and overlaid on (A). (C) The AF image has been registered with the fundus photograph and segmented in (D) into atrophic regions with decreased fluorescence, purple, and FIAF, pink, regions. The FIAF lesions, with excess lipofuscin, may precede atrophy.

(E)Drusen (green) overlaid on the contrast-enhanced image. (G) Same detail of AF image (D).

(F)Hypofluorescent (28% of this section) and hyperfluorescent (13%) regions are overlaid as fluorescent “stains” on the isolated drusen. Total drusen (28% of this section) are thus

Infrared Imaging

Near infrared (IR) reflectance imaging can also be performed with the confocal SLO (cSLO). IR imaging of the macula by cSLO was first aimed at the detection of drusen and subretinal structures (43–47). Kirkpatrick (43) found IR less reliable than photography for drusen identification. Elsner (44) demonstrated the presence of small (<25 ) hyperreflectant subretinal deposits in most normal subjects over age 20 and in all AMD subjects. These deposits were not visible in photographs, but larger deposits in AMD subjects correlated with photographically visible drusen. IR images of patients with early STGD show more abnormalities than do photographs (48), owing to the sensitivity of IR imaging to pigmentation of the RPE.

A precise accounting of all AMD lesions in a given macula also provides the best possible phenotypic data to correlate with environmental and genetic risk factors. In a complex disorder like AMD, with widely varying phenotypes and multiple risk factors, these relationships can be uncovered only with very large data sets such as in the AREDS study. From this will flow a wealth of quantitative relationships, thereby improving the likelihood of discovering significant phenotype–genotype correlations. The discovery of specific DNA mutations associated with AMD lesions could form the basis for early diagnosis of individuals at risk, as well as the development of therapies based on specific molecular defects. These advances would extend profound health and social benefits to our aging population.

As the parallel tasks of segmenting drusen and RPE abnormalities in color fundus photographs and analyzing AF and IR abnormalities in SLO images proceed, image registration directly compares the features in these three imaging modalities. Segmentation and registration of macular images permits construction of disease metrics for understanding the relationships between disease components. Early work by Goldbaum et al. and Hart et al. (49,50) pointed this out. Many general registration techniques are available (51). Montaging and mosaicing (52–55) have also been explored for rapid image alignment. Unfortunately, systematic research using ophthalmic image registration to study disease processes has just begun. For example, Lois et al. studied the relationship of AF abnormalities with drusen in a completely subjective manner (40) and concluded that they were essentially independent. However, image registration and quantitative lesion segmentation demonstrated significant co-localization of drusen and hyper AF in Stage 3 AMD (42). In view of the importance of AF, registration of photographs, and SLO images for data fusion, an established technique in computer vision (56–58) would seem essential for understanding disease mechanisms. Digital registration of serial fundus photographs for analysis of AMD lesions such as drusen or GA, carries obvious advantages (58), but manual methods continue to predominate (59,60).

Fig. 2. (Continued) subdivided into normo- (15%), hyper- (3%), and hypofluorescent (10%) drusen. In particular, the coincidence of drusen and FIAF (3%) is a small portion of this section. (H) Reversing the overlay (placing the drusen on top of the FIAF) demonstrates the more extensive FIAF (pink) adjacent to, but not within, the drusen and GA (10% of this section). The remainder (3%) of the hyper AF that co-localized with drusen hence comprises only 3 out of 13 (23% )of the total FIAF.

Digital Fundus Photography and Segmentation of Drusen

Digital image analysis techniques faced three significant obstacles in drusen identification (43,61–66). First, the inherent nature of the reflectance of the normal macula is nonuniform. There is less reflectance centrally and increasing reflectance moving out towards the arcades. Local threshold approaches to drusen segmentation met with only partial success because the background variability limited the extent to which purely histogram-based methods could succeed. This increased the need for operator intervention and has been the main obstacle to automating drusen segmentation.

The second major obstacle to drusen identification has been that of object recognition. A computer must ultimately learn to differentiate drusen from areas of retinal pigment epithelial hypopigmentation, exudates and scars. Goldbaum suggested subtleties of coloration and shape as modes of automated recognition (67). However, this subject has not been developed further and, at present, the complete attention of the operator during the preprocessing phase is required to exclude such confounders in approx 20% of images (68).

The third major obstacle to drusen identification and equally challenging is that of boundary definition: soft, indistinct drusen have no precise boundary and, thereby, the solution to their segmentation, by definition, cannot be precise. The central color fades into the background peripherally, and on stereo viewing there is no well-defined edge. Soft drusen, that are confluent or coalesce as part of the natural course of AMD are particularly “boundaryless.” Practical segmentation of drusen then requires that areas of drusen determined by a digital method agree, in aggregate, with the judgments of a qualified grader. This approach was adopted by Shin et al. for validation of their method (61). However, expert manual drawings themselves are necessarily variable. In some cases, expert manual drawing can vary as much as digital segmentation methods. Indeed, specificity and sensitivity calculations for expert manual drawings of two retinal experts can demonstrate significant inter-observer differences (69). Therefore, achieving comparable accuracy in automated drusen segmentation relative to an acceptable stereo viewing expert grader remains a laudable goal during refinement of the digital segmentation methods.

An objection to digital techniques generally might be loss of information compared to color slides, but recent work by Lee et al. and Scholl et al. has demonstrated that even compressed images perform well for drusen identification (70,71).

Complex images combining drusen and RPE hypopigmentation are more difficult, because these lesions may not be separable on green channel reflectance alone (63). The use of red-green color space discriminants has been suggested (67) with very limited results. Modern neural net techniques and matrix factorization techniques merit investigation (72–74). Now, we will take a closer look at digital segmentation methods as they have been developed thus far.

AUTOMATED DRUSEN MEASUREMENT BY THE MATHEMATICAL BACKGROUND MODEL

The Concept

The key concept in this method, which is called background leveling, uses a mathematical model to reconstruct the macular background from selected subsets and then

remove this background variability from the entire image. This allows global threshold selection for uniform object identification, freed from the local thresholding obstacle that had so far prevented digital methods from being effective. This concept is quite general, i.e., not restricted to ophthalmic images, and appears to be original in the medical and imaging literature. In the macular applications, the algorithm gains additional power by exploiting the specific geometry of macular reflectance, as will be described. Further, despite the past inadequacy of adaptive histogram techniques, they become much more effective for automated thresholding after the model has leveled the background.

The first step was to demonstrate that the mathematical model, quadratic polynomials in several zones with cubic spline interpolation in blending regions between the zones, can approximate the global macular image background of a normal photograph or AF image with sufficient accuracy to allow its reconstruction and leveling (75–77). The next step was to show that the model, operating on user-defined subsets of background data in abnormal images, was still capable of accurately leveling the background for reliable segmentation of drusen (78). Finally, background leveling was combined with the wellknown histogram-based Otsu method (79) for background selection and final threshold selection to achieve a largely automated method of drusen segmentation (69).

Algorithm for Drusen Segmentation

The following synopsis of the key steps in the automated algorithm illustrates these principles. Many elementary image-processing details are omitted and can be found in Smith et al. (69).

In this example, the region studied was the central 3000- m diameter circle (the combined central and middle subfields defined by the Wisconsin grading template: central subfield, the 1-mm circle of diameter; middle subfield, the 3-mm annulus of outer diameter). The green channel, in which drusen have the greatest contrast, is extracted from a high-resolution digital fundus photograph or digitized film photograph.

Operator Preprocessing

Any potential confounding lesions such as GA or marked RPE hypopigmentation with elevated green channel values are removed manually. It has been estimated this would be necessary in about 20% of cases (68).

Luteal Compensation

The first step is a luteal pigment correction applied to the green channel of the standardized image. The ratio of the median values of the histograms of the green channel in the middle and central subfields was calculated. This ratio was applied to a Gaussian distribution centered on the fovea and having a half-maximum at 600- m diameter. The green channel was multiplied by this Gaussian to produce the luteal compensated image. All further processing and segmentation was carried out on this image.

Two Zone Math Model

Zone 1 is the central subfield, Zone 2 the annulus of diameters 1000 and 3000 m. The pixel gray levels were considered to be functions of their pixel coordinates (x,y) in the x–y plane. The general quadratic q (x, y) = ax2 + bxy + cy2 + dx + ey + constant in two variables was fit by custom software employing least squares methods to any chosen

background input of green channel gray levels to optimize the six coefficients (a, b, c, d, e, constant) (76). In this case, the model consisted of a set of two quadratics, one for each zone, with cubic spline interpolations at the boundary (76).

Initial Background Selection by Otsu Method

The automatic histogram based thresholding technique known as the Otsu method (79) in each zone provided initial input to the background model. Briefly, let the pixels in the green channel be represented in L gray levels [1, 2,…,L]. Suppose the pixels are dichotomized into two classes C0 and C1 by a threshold at level k. C0 denotes pixels with levels [1,…,k] and C1 denotes pixels with levels [k + 1,…,L]. Ideally, C0 and C1 would represent background and drusen. The Otsu method uses the criterion of between-class variance and selects the threshold k that maximizes this variance (79).

The Otsu method can be generalized to the case of two thresholds k and m, where there are three classes C0, C1, and C2 defined by pixels with levels [1,…,k], [k + 1,…,m], and [m + 1,…,L], respectively. In a given image, these classes might represent background, objects of interest, and other objects (e.g., retinal vessels), in some permutation. The criterion for class separability is the total between-class variance.

The Otsu method may also be performed sequentially to subdivide a given class. That is, if a given class C is already defined, then C may be treated as the initial histogram and one can apply an Otsu method to subdivide C into two (or three) classes.

Operator Choices (Supervision)

The two-threshold Otsu method was used to provide an initial segmentation by thresholds k and m in each region into three desired classes: C0 (dark nonbackground sources, e.g., vessels and pigment), C1 (background), and C2 (drusen). In particular, for each region there was an initial choice of background, C1, for input to the mathematical background model. The operator could also modify the Otsu method by choosing among two other options: (1) If multiple large, soft, ill-defined drusen were present, the upper (drusen) thresholds were each reduced by four gray levels; (2) If few drusen (5% range) were present in a region, the drusen class C2 was subdivided again by the single threshold Otsu method, with the higher values becoming the new C2 and the lower values included in C1. These were the only operator decisions needed to determine C1 (the background) for input to the model. The rest of the algorithm up to final segmentation was completely automatic.

Background Leveling and Iteration

Let Z be the luteal corrected image data and let Q be the two quadratic, two-zone model blended by cubic splines at the 1000- m boundary, that was fit to the background data C1 in each zone determined by the Otsu method specified previously. The first leveled image Z1 is defined as

Z1 = Z – Q + 125

The constant offset 125 maintains an image with mean approx 125. The process can now also be iterated if desired, with the leveled image input to this algorithm. The final drusen segmentation was then obtained by applying the specified Otsu method to the final leveled image and removing any confounding lesions identified in manual preprocessing. In practice, the final results changed little after two iterations of the leveling process.

Validation

Validation with the established standard of stereo fundus photo grading was obtained manually as follows: a retinal expert drew on a graphic tablet the boundaries of all lesions identified in a suitably contrast-enhanced image. As the user drew, the 1-pixel pencil tool in Photoshop (Photoshop 7.0, Adobe Systems Inc., San Jose, CA) outlined the lesions in a transparent digital layer. Reference was also made as needed to the stereo fundus photographs to determine the exact boundary. Drusen areas were measured, and in cases in which the experts disagreed by more than 5%, the two graders collaborated to redraw to consensus. On a total of 20 images, the drusen areas were also measured by the automated method and compared to a stereo viewing drawing of an expert grader. False-positive pixels (drusen areas found by the automated method but not selected by the retinal expert) and false-negative pixels (drusen areas selected by the retinal expert but not selected by the automated method) were also identified. The specificity and sensitivity of the automated method were 0.81 and 0.70, respectively.

APPLICATION: SEGMENTATION AND CO-LOCALIZATION OF DRUSEN AND AUTOFLUORESCENCE

The model was used to level the background and perform precise segmentation of drusen in fundus photographs and FIAF in SLO images from a patients with Stage 3 AMD (large, soft drusen and no pigment abnormalities). A close relationship was demonstrated in the registered images between the spatial distribution of drusen and FIAF (Fig. 1). In other patients with Stage 4 AMD (large, soft drusen and GA), the FIAF occurs in the border zone of the GA and adjacent to some drusen, but no longer coincident with the drusen (Fig. 2). This small series suggested that focal lipofuscin accumulation in AMD as measured by autofluorescence largely co-localizes with soft drusen in Stage 3 and then the relationship changes in the presence of pigment abnormalities. This suggested that dispersal of drusen-associated lipofuscin may be a marker for disease progression in AMD.

THE FUTURE OF MACULAR IMAGE ANALYSIS

The future lies in the further development of efficient algorithms for segmentation, classification, and quantitative analysis of retinal images in register, each carrying different information, to yield powerful tools for quantitative macular research and telemedical applications for early disease detection. The global mathematical models described herein for photographs and SLO images will be applied to image data from techniques such as fluorescence lifetime (80) and hyperspectral (81,82) imaging not yet deployed in AMD research. With these data researchers will move beyond lesion identification and be able to identify in vivo individual molecular signatures. As we exploit new information from disease metrics based on data fusion from all these images in registration, there will inevitably be process failures and sources of outcome variability such as lens effects. New algorithms will be developed to remedy them.

Background leveling may be useful as a preliminary step before other image analysis tools are employed (83,84). Modern hierarchical neural networks and non-negative matrix factorizations for spectral decomposition (72–74) are among many powerful tools in Machine Vision and Pattern Recognition that have not yet reached retinal image analysis. These digital algorithms will yield scalable integrated systems suitable for

high throughput in clinical trials and the potential for a deeper understanding of the pathological pathways and genetic basis of macular disease.

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