Automated segmentation of pathological cavities in optical coherence tomography scans

To develop and evaluate a method for automated segmentation and quantitative analysis of pathological cavities in the retina visualized by spectral-domain optical coherence tomography (SD-OCT) scans.

The algorithm is based on the segmentation of the gray-level intensities within a B-scan by a k-means cluster analysis and subsequent classification by a k-nearest neighbor algorithm. Accuracy was evaluated against three clinical experts using 130 bullous cavities identified on eight SD-OCT B-scans of three patients with wet age-related macular degeneration (AMD) and five patients with X-linked retinoschisis, as well as on one volume scan of a patient with X-linked retinoschisis. The algorithm calculated the surface area of the cavities for the B-scans and the volume of all cavities for the volume scan. In order to validate the applicability of the algorithm in clinical use, we analyzed 31 volume scans taken over the course of 4 years for one AMD patient with a serous retinal detachment.

Discrepancies in area measurements between the segmentation results of the algorithm and the experts were within the range of the area deviations among the experts. Volumes interpolated from the B-scan series of the volume scan were comparable among experts and algorithm (0.249 mm³ for the algorithm, 0.271 mm³ for expert 1, 0.239 mm³ for expert 2, and 0.262 mm³ for expert 3). Volume changes of the serous retinal detachment were quantifiable.

The segmentation algorithm represents a method for the automated analysis of large numbers of volume scans during routine diagnostics and in clinical trials.