BACKGROUND: Artificial neural networks (ANNs) have been used to classify eye diseases, such as diabetic retinopathy (DR) and glaucoma. DR is the leading cause of blindness in working-age adults in the developed world. The implementation of DR diagnostic routines could be feasibly improved by the integration of structural and optical property test measurements of the retinal structure that provide important and complementary information for reaching a diagnosis. In this study, we evaluate the capability of several structural and optical features (thickness, total reflectance and fractal dimension) of various intraretinal layers extracted from optical coherence tomography images to train a Bayesian ANN to discriminate between healthy and diabetic eyes with and with no mild retinopathy. RESULTS: When exploring the probability as to whether the subject's eye was healthy (diagnostic condition, Test 1), we found that the structural and optical property features of the outer plexiform layer (OPL) and the complex formed by the ganglion cell and inner plexiform layers (GCL + IPL) provided the highest probability (positive predictive value (PPV) of 91% and 89%, respectively) for the proportion of patients with positive test results (healthy condition) who were correctly diagnosed (Test 1). The true negative, TP and PPV values remained stable despite the different sizes of training data sets (Test 2). The sensitivity, specificity and PPV were greater or close to 0.70 for the retinal nerve fiber layer's features, photoreceptor outer segments and retinal pigment epithelium when 23 diabetic eyes with mild retinopathy were mixed with 38 diabetic eyes with no retinopathy (Test 3). CONCLUSIONS: A Bayesian ANN trained on structural and optical features from optical coherence tomography data can successfully discriminate between healthy and diabetic eyes with and with no retinopathy. The fractal dimension of the OPL and the GCL + IPL complex predicted by the Bayesian radial basis function network provides better diagnostic utility to classify diabetic eyes with mild retinopathy. Moreover, the thickness and fractal dimension parameters of the retinal nerve fiber layer, photoreceptor outer segments and retinal pigment epithelium show promise for the diagnostic classification between diabetic eyes with and with no mild retinopathy.

In this paper we present a hybrid probabilistic framework for 3D image segmentation, using Conditional Random Fields (CRFs) and implicit deformable models. Our 3D deformable model uses voxel intensity and higher scale textures as data-driven terms, while the shape is formulated implicitly using the Euclidean distance transform. The data-driven terms are used as observations in a 3D discriminative CRF, which drives the model evolution based on a simple graphical model. In this way, we solve the model evolution as a joint MAP estimation problem for the 3D label field of the CRF and the 3D shape of the deformable model. We demonstrate the performance of our approach in the estimation of the volume of the human tear menisci from images obtained with optical coherence tomography.

The dynamic variation of the human tear meniscus (tears around the eye lids) is very critical in visual function, maintenance of corneal integrity, and ocular comfort. The quantitative measuring of the tear menisci around the eyelids is though a challenging task. In our work, tear meniscus images are obtained with our custom-built optical coherence tomography (OCT) and are processed using our novel segmentation method. For the latter, we use an implicit deformable model driven by a conditional random field (CRF). The evolution of the model is solved as MAP estimation. The target conditional probability is decomposed using a simple graphical model, where the probability field of the pixel labels given the image observations is estimated using a discriminative CRF. Our results show that our segmentation approach successfully handles clutter and boundary ambiguities of the tear menisci, which makes our integrated system reliable for the every day medical practice.