Computer-aided analyses of mouse retinal OCT images - an actual application report

There is a need for automated retinal optical coherence tomography (OCT) image analysis tools for quantitative measurements in small animals. Some image processing techniques for retinal layer analysis have been developed, but reports about how useful those techniques are in actual animal studies are rare. This paper presents the use of a retinal layer detection method we developed in an actual mouse study that involves wild type and mutated mice carrying photoreceptor degeneration.

Spectral domain OCT scanning was performed by four experimenters over 12 months on 45 mouse eyes that were wild-type, deficient for ephrin-A2 and ephrin-A3, deficient for rhodopsin, or deficient for rhodopsin, ephrin-A2 and ephrin-A3. The thickness of photoreceptor complex between the outer plexiform layer and retinal pigment epithelium was measured on two sides of the optic disc as the biomarker of retinal degeneration. All the layer detection results were visually confirmed.

Overall, 96% (8519 out of 9000) of the half-side images were successfully processed using our technique in a semi-automatic manner. There was no significant difference in success rate between mouse lines (p = 0.91). Based on a human observer's rating of image quality for images successfully and unsuccessfully processed, the odds ratios for 'easily visible' images and 'not clear' images to be successfully processed is 62 and 4, respectively, against 'indistinguishable' images. Thickness of photoreceptor complex was significantly different across the quadrants compared (p < 0.001). It was also found that the average thickness based on 4-point sparse sampling was not significantly different from the full analysis, while the range of differences between the two methods could be up to about 6 μm or 16% for individual eyes. Differences between mouse lines and progressive thickness reduction were revealed by both sampling measures.

Although the thickness of the photoreceptor complex layer is not even, manual sparse sampling may be as sufficiently accurate as full analysis in some studies such as ours, where the error of sparse sampling was much smaller than the effect size of rhodopsin deficiency. It is also suggested that the image processing method can be useful in actual animal studies. Even for images poorly visible to human eyes the image processing method still has a good chance to extract the complex layer.