Edward Chaum

Detection of Anatomic Structures in Human Retinal Imagery

The widespread availability of electronic imaging devices throughout the medical community is leading to a growing body of research on image processing and analysis to diagnose retinal disease such as diabetic retinopathy (DR). Productive computer-based screening of large, at-risk populations at low cost requires robust, automated image analysis. In this paper we present results for the automatic detection of the optic nerve and localization of the macula using digital red-free fundus photography. Our method relies on the accurate segmentation of the vasculature of the retina followed by the determination of spatial features describing the density, average thickness, and average orientation of the vasculature in relation to the position of the optic nerve. Localization of the macula follows using knowledge of the optic nerve location to detect the horizontal raphe of the retina using a geometric model of the vasculature. We report 90.4% detection performance for the optic nerve and 92.5% localization performance for the macula for red-free fundus images representing a population of 345 images corresponding to 269 patients with 18 different pathologies associated with DR and other common retinal diseases such as age-related macular degeneration.

Exudate-based diabetic macular edema detection in fundus images using publicly available datasets

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME through the presence of exudation. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing (e.g., the classifier was trained on an independent dataset and tested on MESSIDOR). Our algorithm obtained an AUC between 0.88 and 0.94 depending on the dataset/features used. Additionally, it does not need ground truth at lesion level to reject false positives and is computationally efficient, as it generates a diagnosis on an average of 4.4s (9.3s, considering the optic nerve localisation) per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

Retinal neuroprotection by growth factors: A mechanistic perspective

For more than a decade it has been known that certain growth factors inhibit apoptosis in genetically determined and experimental models of inner and outer retinal degeneration. The molecular mechanisms underlying these protective effects and the signaling that supports the survival of photoreceptors and retinal ganglion cells in these models have recently come under more in depth investigation. This paper reviews our current understanding of the balance of pro‐ and antiapoptotic signals that determine cell fate in the retina and how the activation of key signal transduction pathways by specific classes of neurotrophins protects retinal neurons.

Automated diagnosis of retinopathy by content-based image retrieval.

Purpose: To describe a novel computer-based image analysis method that is being developed to assist and automate the diagnosis of retinal disease. Methods: Content-based image retrieval is the process of retrieving related images from large database collections using their pictorial content. The content feature list becomes the index for storage, search, and retrieval of related images from a library based upon specific visual characteristics. Low-level analyses use feature description models and higher-level analyses use perceptual organization and spatial relationships, including clinical metadata, to extract semantic information. Results: We defined, extracted, and tested a large number of region- and lesion-based features from a dataset of 395 retinal images. Using a statistical hold-one-out method, independent queries for each image were submitted to the system and a diagnostic prediction was formulated. The diagnostic sensitivity for all stratified levels of age-related macular degeneration ranged from 75% to 100%. Similarly, the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7% and for nonproliferative diabetic retinopathy, ranged from 75% to 94.7%. The overall purity of the diagnosis (specificity) for all disease states in the dataset was 91.3%. Conclusions: The probabilistic nature of content-based image retrieval permits us to make statistically relevant predictions regarding the presence, severity, and manifestations of common retinal diseases from digital images in an automated and deterministic manner.

Automatic retina exudates segmentation without a manually labelled training set

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step; therefore, they do not require labelled lesion training sets which are time consuming to create, difficult to obtain and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. We evaluate our algorithm with this dataset and compare our results with two recent exudate segmentation algorithms. In all of our tests, our algorithms perform better or comparable with an order of magnitude reduction in computational time.

Telehealth practice recommendations for diabetic retinopathy, second edition.

Ocular telemedicine and telehealth have the potential to decrease vision loss from DR. Planning, execution, and follow-up are key factors for success. Telemedicine is complex, requiring the services of expert teams working collaboratively to provide care matching the quality of conventional clinical settings. Improving access and outcomes, however, makes telemedicine a valuable tool for our diabetic patients. Programs that focus on patient needs, consider available resources, define clear goals, promote informed expectations, appropriately train personnel, and adhere to regulatory and statutory requirements have the highest chance of achieving success.

Elliptical local vessel density: A fast and robust quality metric for retinal images

A great effort of the research community is geared towards the creation of an automatic screening system able to promptly detect diabetic retinopathy with the use of fundus cameras. In addition, there are some documented approaches for automatically judging the image quality. We propose a new set of features independent of field of view or resolution to describe the morphology of the patients vessels. Our initial results suggest that these features can be used to estimate the image quality in a time one order of magnitude shorter than previous techniques.

Cytogenetic analysis of retinoblastoma: evidence for multifocal origin and in vivo gene amplification

Retinoblastoma (Rb) is an uncommon childhood tumor of the neural retina with a significant genetic component in its etiology. A small proportion of patients have a deletion in chromosome 13 encompassing band 13q14, an observation which permitted the assignment of the RB1 locus to this region. About 20% of Rb tumors exhibit microscopic deletions of band 13q14 or monosomy 13. Trisomy 1q and i(6p) have also been reported in a high percentage of tumors. We analyzed the chromosome complements from direct preparations of 10 Rb tumors derived from seven patients. Modal chromosome numbers ranged from 45 to 48, and occasional duplications of the genomes were noted. In general, the tumors were chromosomally stable, although karyotypic evolution and random chromosome loss were encountered. Consistent abnormalities included trisomy 1q, i(6p), 6q-, and del(13)(q12----14). One patient with bilateral Rb had three tumor clones (two in one eye and one in the other) with chromosome abnormalities unrelated in origin. A second patient with unilateral Rb had two tumor clones with chromosome abnormalities again unrelated in origin. These two patients provide some of the first cytogenetic evidence for the multifocal origin of primary Rb. In the untreated tumor of a third patient, a homogeneously staining region (HSR) was detected in 1p32, indicating gene amplication in vivo; previously, an HSR at this site has been reported in the established Rb cell line Y79.

Validating retinal fundus image analysis algorithms: Issues and a proposal

This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison.

Age-related susceptibility to apoptosis in human retinal pigment epithelial cells is triggered by disruption of p53-Mdm2 association.

PURPOSE Relatively little is known about the contribution of p53/Mdm2 pathway in apoptosis of retinal pigment epithelial (RPE) cells or its possible link to dysfunction of aging RPE or to related blinding disorders such as age-related macular degeneration (AMD). METHODS Age-associated changes in p53 activation were evaluated in primary RPE cultures from human donor eyes of various ages. Apoptosis was evaluated by activation of caspases and DNA fragmentation. Gene-specific small interfering RNA was used to knock down expression of p53. RESULTS We observed that the basal rate of p53-dependent apoptosis increased in an age-dependent manner in human RPE. The age-dependent increase in apoptosis was linked to alterations in several aspects of the p53 pathway. p53 phosphorylation Ser15 was increased through the stimulation of ATM-Ser1981. p53 acetylation Lys379 was increased through the inhibition of SIRT1/2. These two posttranslational modifications of p53 blocked the sequestration of p53 by Mdm2, thus resulting in an increase in free p53 and of p53 stimulation of apoptosis through increased expression of PUMA (p53 upregulated modulator of apoptosis) and activation of caspase-3. Aged RPE also had reduced expression of antiapoptotic Bcl-2, which contributed to the increase in apoptosis. Of particular interest in these studies was that pharmacologic treatments to block p53 phosphorylation, acetylation, or expression were able to protect RPE cells from apoptosis. CONCLUSIONS Our studies suggest that aging in the RPE leads to alterations of specific checkpoints in the apoptotic pathway, which may represent important molecular targets for the treatment of RPE-related aging disorders such as AMD.