Automatic Production of Synthetic Labeled OCT Images Using Active Shape Model

The challenge of limited labeled data in the field of medical imaging and the need for large number of labeled data for training machine learning algorithms, and to measure the performance of image processing algorithms increases the demand to use synthetic images. The purpose of this paper is to construct synthetic and labeled Optical Coherence Tomography (OCT) data to solve the problems like having access to the accurate labeled data and evaluating the processing algorithms. In this study, a modified active shape model is used which considers the anatomical features of available images such as number and thickness of the layers and their associated brightness, the retinal blood vessels, and shadow information with wise consideration of speckle noise. The algorithm is also able to provide different datasets with varying noise level. The validity of our method for synthesis of retinal images is measured by two methods (qualitative assessment and quantitative analysis).