RESUMEN
BACKGROUND: Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer professional-level information like pigment distribution maps, but encounter practical limitations such as complex system requirements. METHODS: This study introduces a spectrum-based framework for training a deep learning model to generate melanin and hemoglobin distribution maps from skin images. This approach eliminates the need for manually prepared ground truth by synthesizing output maps into skin images for regression analysis. The framework is applied to acquire spectral data, create pigment distribution maps, and simulate pigment variations. RESULTS: Our model generated reflectance spectra and spectral images that accurately reflect pigment absorption properties, outperforming spectral upsampling methods. It produced pigment distribution maps with correlation coefficients of 0.913 for melanin and 0.941 for hemoglobin compared to the VISIA system. Additionally, the model's simulated images of pigment variations exhibited a proportional correlation with adjustments made to pigment levels. These evaluations are based on pigment absorption properties, the Individual Typology Angle (ITA), and pigment indices. CONCLUSION: The model produces pigment distribution maps comparable to those from specialized clinical equipment and simulated images with numerically adjusted pigment variations. This approach demonstrates significant promise for developing professional-level diagnostic tools for future clinical applications.
Asunto(s)
Aprendizaje Profundo , Melaninas , Humanos , Melaninas/química , Hemoglobinas/química , Pigmentación de la Piel , Piel/diagnóstico por imagen , Piel/química , Piel/metabolismo , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
BACKGROUND: Skin tone and pigmented regions, associated with melanin and hemoglobin, are critical indicators of skin condition. While most prior research focuses on pigment analysis, the capability to simulate diverse pigmentation conditions could greatly broaden the range of applications. However, current methodologies have limitations in terms of numerical control and versatility. METHODS: We introduce a hybrid technique that integrates optical methods with deep learning to produce skin tone and pigmented region-modified images with numerical control. The pigment discrimination model produces melanin, hemoglobin, and shading maps from skin images. The outputs are reconstructed into skin images using a forward problem-solving approach, with model training aimed at minimizing the discrepancy between the reconstructed and input images. By adjusting the melanin and hemoglobin maps, we create pigment-modified images, allowing precise control over changes in melanin and hemoglobin levels. Changes in pigmentation are quantified using the individual typology angle (ITA) for skin tone and melanin and erythema indices for pigmented regions, validating the intended modifications. RESULTS: The pigment discrimination model achieved correlation coefficients with clinical equipment of 0.915 for melanin and 0.931 for hemoglobin. The alterations in the melanin and hemoglobin maps exhibit a proportional correlation with the ITA and pigment indices in both quantitative and qualitative assessments. Additionally, regions overlaying melanin and hemoglobin are demonstrated to verify independent adjustments. CONCLUSION: The proposed method offers an approach to generate modified images of skin tone and pigmented regions. Potential applications include visualizing alterations for clinical assessments, simulating the effects of skincare products, and generating datasets for deep learning.
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Trastornos de la Pigmentación , Pigmentación de la Piel , Humanos , Melaninas/análisis , Piel/diagnóstico por imagen , Piel/química , Eritema , Hemoglobinas/análisisRESUMEN
This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth-like outputs, the input image resolution is restricted by computational resources. The optical approach-based regression method can alleviate this problem, but compromises performance. We propose a strategy to overcome the limitation of image resolution while preserving performance by incorporating ground truth within the optical approach-based learning structure. The proposed model decomposes skin images into melanin, hemoglobin, and shading maps, reconstructing them by solving the forward problem with reference to the ground truth for pigments. Evaluation against the VISIA system, a professional diagnostic equipment, yields correlation coefficients of 0.978 for melanin and 0.975 for hemoglobin. Furthermore, our model can produce pigment-modified images for applications like simulating treatment effects.
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Aprendizaje Profundo , Melaninas , Piel , Hemoglobinas , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
Significance: Melanin and hemoglobin have been measured as important diagnostic indicators of facial skin conditions for aesthetic and diagnostic purposes. Commercial clinical equipment provides reliable analysis results, but it has several drawbacks: exclusive to the acquisition system, expensive, and computationally intensive. Aim: We propose an approach to alleviate those drawbacks using a deep learning model trained to solve the forward problem of light-tissue interactions. The model is structurally extensible for various light sources and cameras and maintains the input image resolution for medical applications. Approach: A facial image is divided into multiple patches and decomposed into melanin, hemoglobin, shading, and specular maps. The outputs are reconstructed into a facial image by solving the forward problem over skin areas. As learning progresses, the difference between the reconstructed image and input image is reduced, resulting in the melanin and hemoglobin maps becoming closer to their distribution of the input image. Results: The proposed approach was evaluated on 30 subjects using the professional clinical system, VISIA VAESTRO. The correlation coefficients for melanin and hemoglobin were found to be 0.932 and 0.857, respectively. Additionally, this approach was applied to simulated images with varying amounts of melanin and hemoglobin. Conclusion: The proposed approach showed high correlation with the clinical system for analyzing melanin and hemoglobin distribution, indicating its potential for accurate diagnosis. Further calibration studies using clinical equipment can enhance its diagnostic ability. The structurally extensible model makes it a promising tool for various image acquisition conditions.
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Aprendizaje Profundo , Melaninas , Humanos , Piel/diagnóstico por imagen , Cara , HemoglobinasRESUMEN
The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays. However, computational integral imaging methods only represent part of the whole image because the size of virtual lens arrays is much smaller than the given large-scale images when dealing with large-scale images. As a result, the previous approaches produce sub-aperture images with a small field of view and need additional devices for depth information to apply to integral imaging pickup systems. In this paper, we present a single image-based computational RGB-D integral imaging pickup system for a large field of view in real time. The proposed system comprises three steps: deep learning-based automatic depth map estimation from an RGB input image without the help of an additional device, a hierarchical integral imaging system for a large field of view in real time, and post-processing for optimized visualization of the failed pickup area using an inpainting method. Quantitative and qualitative experimental results verify the proposed approach's robustness.
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Algoritmos , Lentes , Imagenología TridimensionalRESUMEN
Skin elasticity has been regarded as one of the main indicators of skin condition. Current measurement devices for skin elasticity are mostly expensive for home-use and should contact the skin surface. As a first step to develop improved methods, we focus on the relation between skin elasticity and the entropy of skin images. Reduced skin elasticity causes wrinkles. It spreads frequency components and increases their randomness in the frequency domain. The randomness is quantified as entropy, which is a measure of the disorder of a system in physics. Therefore, skin elasticity is expected to have a negative relation with entropy. This tendency can be improved by applying penetration depth characteristics according to the wavelength of light. From cheeks and forehead of 12 Korean adults, skin images are acquired with three different light sources (470 nm, 870 nm and broadband light) and skin elasticity is measured. The root mean square error between the measured data and the fitted model is "0.27" (870 nm), "0.49" (broadband light) and "1.42" (470 nm). Furthermore, the results are analyzed by classifying by sex, age and measurement area. This study demonstrates the possibility of developing noncontact home-use devices to measure skin elasticity in the future.
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Envejecimiento de la Piel , Piel , Elasticidad , Entropía , FrenteRESUMEN
Cupping therapy is one form of alternative medicine that is used widely across the world. Although the applications of cupping therapy including pain relief have a 1000-year history, the therapeutic effect of cupping is still questionable due to a lack of scientific evidence. Therefore, in the present study, we embedded a near-infrared spectroscopic sensor into a suction cup to monitor the hemodynamic changes on the treated site while the hemodynamics at the surrounding tissue of the cup was also simultaneously monitored by another near-infrared spectroscopic sensor. The results from 10 healthy male subjects show a dramatic increase of the oxy-hemoglobin (OHb) and deoxy-hemoglobin (RHb) concentrations at the treatment site while the OHb and RHb levels were decreased at the surrounding tissue. Moreover, after the treatment, we observed that the OHb concentrations were maintained at a higher level than before treatment at both sites, which may demonstrate how cupping therapy works for treatment. In summary, the results showed that cupping therapy increases blood volume and tissue oxygenation at the treatment site while those were slightly decreased at the surrounding tissue. This study showed that the embedding of near-infrared spectroscopy in a cupping system could offer a better understanding of the mechanism of cupping therapy.