Abstract
Epiretinal Membrane (ERM) is a disease caused by a thin layer of scar tissue that is formed on the surface of the retina. When this membrane appears over the macula, it can cause distorted or blurred vision. Although normally idiopathic, its presence can also be indicative of other pathologies such as diabetic macular edema or vitreous haemorrhage. ERM removal surgery can preserve more visual acuity the earlier it is performed. For this purpose, we present a fully automatic segmentation system that can help the clinicians to determine the ERM presence and location over the eye fundus using 3D Optical Coherence Tomography (OCT) volumes. The proposed system uses a convolutional neural network architecture to classify patches of the retina surface. All the 2D OCT slices of the 3D OCT volume of a patient are combined to produce an intuitive colour map over the 2D fundus reconstruction, providing a visual representation of the presence of ERM which therefore facilitates the diagnosis and treatment of this relevant eye disease. A total of 2.428 2D OCT slices obtained from 20 OCT 3D volumes was used in this work. To validate the designed methodology, several representative experiments were performed. We obtained satisfactory results with a Dice Coefficient of 0.826 ± 0.112 and a Jaccard Index of 0.714 ± 0.155, proving its applicability for diagnosis purposes. The proposed system also demonstrated its simplicity and competitive performance with respect to other state-of-the-art approaches.
Highlights
Recent advances in computing power, together with the development of new algorithms based on artificial neural networks, are enabling enormous progress in the field ofThe associate editor coordinating the review of this manuscript and approving it for publication was Jinhua Sheng .the automatic analysis of medical imaging, as well as in the development of new and more efficient Computer-Aided Diagnosis (CAD) systems
We propose a fully automatic methodology for the segmentation and characterisation of Epiretinal Membrane (ERM) regions using 3D Optical Coherence Tomography (OCT) volumes
This section covers all the results that were acquired during the training, validation and testing of the models in terms of classification and segmentation metrics
Summary
The automatic analysis of medical imaging, as well as in the development of new and more efficient Computer-Aided Diagnosis (CAD) systems. Image-based CAD systems relied on hand-crafted object features, followed by a statistical classification designed for the task [3]. This often required the development of multiple purpose-built classifiers, involving a long and arduous process of feature adjustment and evaluation. The training process of convolutional neural networks performs this feature-extraction and selection process implicitly from annotated images. It works as an end-to-end system that can be trained directly from the data. The whole process can be simplified and generalised to other similar medical imaging domains
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