Recent advancements in cellular-resolution optical coherence tomography (OCT) have opened up possibilities for high-resolution and non-invasive clinical diagnosis. This study uses a deep learning-based algorithm with fuzzy logic on cross-sectional OCT images for in vivo human skin layer segmentation. Using U-Net as the basic framework, a 5-class segmentation model is developed. With deeply supervised learning objective functions, the global (4 skin layers) and local (nuclei) features were separately considered in designing our multi-class segmentation model. Defuzzification is applied for post-processing on the probability maps of the U-Net output to fine-tune the layer segmentation. As a result, an > 85% Dice coefficient accuracy through 5-fold cross-validation was achieved, enabling quantitative measurements for the healthy human skin structure. Specifically, we calculate the thickness of the stratum corneum, epidermis, and the cross-sectional area of keratinocyte nuclei as 22.71 ± 17.20 µm, 66.44 ± 11.61 µm, and 17.21 ± 9.33 µm2, respectively. These measurements align with clinical findings on human skin structures and can serve as standardized metrics for clinical assessment using OCT imaging.