Semi-automated quantification of retinal IS/OS damage in en-face OCT image. (1st February 2016) - Gattani, Vineet Sunil; Vupparaboina, Kiran Kumar; Patil, Ameya; Chhablani, Jay; Richhariya, Ashutosh; Jana, Soumya
Abstract: A variety of vision ailments are indicated by structural changes in the retinal substructures of the posterior segment of the eye. In particular, integrity of the inner-segment/outer-segment (IS/OS) junction directly relates to the visual acuity. In the en-face optical coherence tomography (OCT) image, IS/OS damage manifests as a dark spot in the foveal region, and its quantification, usually performed by experts, assumes diagnostic significance. In this context, in view of the general scarcity of experts, it becomes imperative to develop algorithmic methods to reduce expert time and effort. Accordingly, we propose a semi-automated method based on level sets. As the energy function, we adopt mutual information which exploits the difference in statistical properties of the lesion and its surroundings. On a dataset of 27 en-face OCT images, segmentation obtained by the proposed algorithm exhibits close visual agreement with that obtained manually. Importantly, our results also match manual results in various statistical criteria. In particular, we achieve a mean Dice coefficient of 85.69%, desirably close to the corresponding observer repeatability index of 89.45%. Finally, we quantify algorithmic accuracy in terms of two quotient measures, defined relative to observer repeatability, which could be used as bases for comparison with future algorithms, even if the latter are tested on disparate datasets. Abstract : Graphical abstract: Abstract : Highlights: Semi-automated method to quantify retinal IS/OS damage using level-sets. Mutual information as energy function to leverage statistics of the lesion. Thorough statistical analysis of algorithmic accuracy vis-à-vis manual method. Algorithmic damage quantification reported vis-à-vis observer repeatability. Quotients are defined to fairly compare among methods tested on disparate datasets.