Auto-Segmentation and Quantification of Non-Cavitated Enamel Caries Imaged with Swept-Source Optical Coherence Tomography.

(1) Background: OCT imaging has been used to assess enamel demineralization in dental research, but it is not yet developed enough to qualify as a diagnostic technique in clinics. The current capabilities of most commercial acquisition software allow for visual and qualitative assessments. There is a need for a fast and verified batch-processing algorithm to segment and analyze demineralized enamel. This study suggests a GUI MATLAB algorithm for the processing and quantitative analysis of demineralized enamel. (2) Methods: A group of artificially demineralized human enamels was in vitro scanned under the OCT, and ROI frames were extracted. By using a selected intensity threshold colormap, - () and - () prismatic demineralization can be segmented. A set of quantitative measurements for the average demineralized depth, average line profile, and integrated reflectivity can be obtained for an accurate assessment. Real and simulated OCT frames were used for algorithm verification. (3) Results: A strong correlation between the automated and known Excel measurements for the average demineralization depth was found (R > 0.97). (4) Conclusions: OCT image segmentation and quantification of the enamel demineralization zones are possible. The algorithm can assess the future development of a real-time assessment of dental diagnostics using an oral probe OCT.