Abstract

We propose an orthogonal-polarization-gating optical coherence tomography (OPG-OCT) for human sweat ducts in vivo. OPG-OCT is composed of the orthogonal linearly polarized light of a sample arm individually interfering with orthogonal linearly polarized lights of the reference arms, where OPG-OCT induces two images, one reflecting the projection intensity and the other the horizontal linear diattenuation (HLD). The results demonstrate that OPG-OCT projection intensity could improve the image quality of sweat ducts. HLD also clearly illustrates the spiral shape of the sweat ducts. Finally, sweat ducts in intensity image are segmented by employing convolutional neural networks (CNN). The proportions of left-handed and right-handed ducts are extracted to characterize the sweat ducts based on HLD. Therefore, the OPG-OCT technique employing CNN for the human sweat glands has the potential to automatically identify the human sweat ducts in vivo.

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