Dark skin-type individuals have a greater tendency to have pigmentary disorders, among which melasma is especially refractory to treat and often recurs. Objective measurement of melanin amount helps evaluate the treatment response of pigmentary disorders. However, naked-eye evaluation is subjective to weariness and bias. We used a cellular resolution full-field optical coherence tomography (FF-OCT) to assess melanin features of melasma lesions and perilesional skin on the cheeks of eight Asian patients. A computer-aided detection (CADe) system is proposed to mark and quantify melanin. This system combines spatial compounding-based denoising convolutional neural networks (SC-DnCNN), and through image processing techniques, various types of melanin features, including area, distribution, intensity, and shape, can be extracted. Through evaluations of the image differences between the lesion and perilesional skin, a distribution-based feature of confetti melanin without layering, two distribution-based features of confetti melanin in stratum spinosum, and a distribution-based feature of grain melanin at the dermal–epidermal junction, statistically significant findings were achieved (p-values = 0.0402, 0.0032, 0.0312, and 0.0426, respectively). FF-OCT enables the real-time observation of melanin features, and the CADe system with SC-DnCNN was a precise and objective tool with which to interpret the area, distribution, intensity, and shape of melanin on FF-OCT images.

Vitiligo, a psychologically distressing pigmentary disorder characterized by white depigmented patches due to melanocyte loss, necessitates non-invasive tools for early detection and treatment response monitoring. High-cellular-resolution full-field optical coherence tomography (CRFF-OCT) is emerging in pigmentary disorder assessment, but its applicability in vitiligo repigmentation after tissue grafting remains unexplored. To investigate the feasibility of CRFF-OCT for evaluating vitiligo lesions following tissue grafting, our investigation involved ten vitiligo patients who underwent suction blister epidermal grafting and laser ablation at a tertiary center between 2021 and 2022. Over a six-month period, clinical features, dermoscopy, and photography data were recorded. Utilizing CRFF-OCT along with artificial intelligence (AI) applications, repigmentation features were captured and analyzed. The CRFF-OCT analysis revealed a distinct dark band in vitiligo lesion skin, indicating melanin loss. Grafted areas exhibited melanocytes with dendrites around the epidermal-dermal junction and hair follicles. CRFF-OCT demonstrated its efficacy in the early detection of melanocyte recovery and accurate melanin quantification. This study introduces CRFF-OCT as a real-time, non-invasive, and in vivo evaluation tool for assessing vitiligo repigmentation, offering valuable insights into pigmentary disorders and treatment responses.

In recent years, deep learning is widely and successfully applied in the medical images which have been established an abundant database in clinical practice. OCT is a relatively new imaging technique and worth in-depth exploration in the deep learning field, however, it is still in an early stage where medical doctors are learning to interpret its images. For shortening the learning curve, this paper used a deep convolutional neural network on a high-resolution full-field OCT system to enhance features in images. By combining with the spatial compounding technique, a noise map prediction method can be employed to discriminate noises from signals and thus increase the image quality. For 100 testing samples, the average of PSNR and SSIM have improved from 20.7 and 0.43 to 26.55 and 0.68 after denoising by the proposed denoising model. Moreover, some important features would be more distinct to support diagnosis in clinical data.