Ординатура / Офтальмология / Английские материалы / Automated Image Detection of Retinal Pathology_Jelinek, Cree_2009

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disease in the same patient between two time points, i.e., progression measurement or staging. Such a system can be built with what I term a bottom-up approach: to simulate as closely as possible the diagnosis or staging according to clinical standards as performed by human experts. For example, a system to diagnose diabetic retinopathy will detect the lesions that are known to clinicians to be related to diabetic retinopathy, such as hemorrhages and microaneurysms, and use the knowledge about the presence or absence of these lesions to estimate the probability of the presence of diabetic retinopathy. The advantages of this bottom-up approach are:

•face validity, i.e., clinicians trust the system because it mimics to some degree what they do daily, easing translation of such a system into clinical practice.

•availability of evidence showing that these classifications are valid, in other words, allow progression or outcome or need for intervention to be measured adequately. This avoids the need to evaluate the system against measures of disease or severity. Examples include the ETDRS [6] and the International Disease Severity Scale of diabetic retinopathy [12] gradings for diabetic retinopathy.

The disadvantages of the bottom-up approach are:

•a need to mimic the human visual system as closely as possible.

•a possibility of bias: the human expert may not be the optimal system for diagnosis or staging and not be fully knowledgeable or aware of all the features in the image.

•inflexibility: in the case in which imaging modalities, such as 3D Optical Coherence Tomography, are developed, human experts may not yet know what the important features are, and there is nothing to be mimicked.

Most research on computer (aided) diagnosis and staging from retinal images has used the bottom-up approach.

What we will term the top-down approach is to start with images in the one hand and diagnoses/stages or some other measurement of clinical outcome in the other, and design the system in an unbiased fashion by searching the feature detector space for the optimal characteristics of the image to predict the measurement of clinical outcome. In other words, those features of the images that are used by clinicians are simply ignored, and the system is optimized against the measurement of clinical outcome, without an intervening stage of human expert-based lesion detection.

The advantages are:

•theoretically, such a system has optimal performance that a bottom-up system cannot reach.

•if new characteristics or features are found that were not previously appreciated, these can help guide clinicians or researchers understand why these characteristics are so important.

•the system can be used on new imaging modalities, where clinicians do not (yet) know what is relevant for diagnosis or progression.

The disadvantages are:

•the system may not have face validity, making clinicians reluctant to trust it in clinical practice.

•the space of potential feature detectors may be huge, and intractable computationally.

To our knowledge, no systems based on such a top-down approach for diagnosis or staging of retinal disease from retinal images have been published. In the remainder of this chapter, we will therefore limit ourselves to a brief review of bottom-up systems.

3.3Detection of Diabetic Retinopathy Lesions

We briefly review here the characteristic lesions that occur on the retina affected in diabetic retinopathy [8], and their approximate frequency in a typical screening population, in which 5 to 25% have some form of diabetic retinopathy [5; 8; 13–15].

•so-called “red lesions,” namely microaneurysms and intraretinal hemorrhages (frequency 3:5%).

•so-called “bright lesions,” namely exudates and cotton wool spots (most likely intraretinal infarcts) (frequency 1:6%).

•retinal thickening if present without bright or red lesions (frequency 1%).

•vascular beading (caliber changes of the vessels) (frequency 1%).

•intraretinal microvascular abnormalities (IRMA) (frequency 1%).

•new vessel growth on the optic disc (neovascularization of the disc or NVD) (frequency < 0:1%).

•new vessel growth elsewhere (NVE) (frequency < 0:1%).

•vitreous or subhyaloid hemorrhage (frequency < 0:1%).

In previous papers, other authors as well as our group have referred to microaneurysms and intraretinal hemorrhages as “red lesions,” to exudates and cotton wool spots as “bright lesions,” and IRMA, NVD, and NVE as “neovascularizations.” In most regions in the world, screening is performed with nonstereo digital cameras, which means that it is impossible to detect retinal thickening if no bright or red lesions are present. Neovascularizations are rare, but are important because they need urgent referral for management by an ophthalmologist. In the next section, we review the literature on how each of these objects can be detected.

3.4Detection of Lesions and Segmentation of Retinal Anatomy

Research of methods for segmentation of retinal vessels shows that the most recent methods can segment over 99% or more of the larger vessels (arbitrarily stated here as > 2 pixels wide) [9; 11; 16–28], even in retinal images that exhibit some to severe retinopathy. The main research emphasis is on making these methods more robust in the face of more severe retinal disease, making them independent of image scale as a parameter, improving the time and memory efficiency of the algorithms, and finally detecting vessel abnormalities [29; 30].

Segmentation of red lesions (microaneurysms and hemorrhages) has also proceeded rapidly, to the point where combined sensitivity/specificity of 95%/95% are reported [16; 31–39].

Bright lesions (exudates and cotton wool spots) have received less attention, but even there, detection rates of up to 95% have been reported [40–46].

Neovascularizations have usually been grouped with red lesions, but merit a separate algorithm, because these can be subtle, which to date has not yet been published.

Many authors have successfully studied the localization of retinal landmarks, including the optic disc, large vessels, and fovea [16; 17; 42; 47–52]. In summary, as of 2007, detection of most lesions is possible with a degree of accuracy that compares favorably to human experts. We have not dealt with determination of image quality, a very important subject that is briefly discussed in Chapter 6 in regard to microaneurysm detection.

3.5Detection and Staging of Diabetic Retinopathy: Pixel to Patient

Either implicitly or explicitly, any bottom-up system must have stages where pixels are classified into lesions or textures. Lesions or textures are detected in individual images, the presence or absence of different lesions or textures is used to determine an estimate for the presence or stage of the retinopathy for a patient. Complete systems have been published, but the authors usually have not dealt explicitly with how pixel features, lesion or texture features, and lesion location estimates are combined into this patient level estimate statement [37; 41; 44; 50; 53–55]. We have designed a supervised method for combining the output of bright-lesion and image quality algorithms and tested it on 10 000 exams (40 000 retinal images) from a community diabetic retinopathy screening project. Retinal images came from a variety of nonmydriatic camera types, as is typical for screening projects, and were read by only a single reader. The results of this preliminary study showed that this system could obtain an area under the ROC (receiver operating characteristic) curve of 0.84 [56]. Meanwhile we have made further improvements to the system, and at ARVO 2008