Background and Objectives: Area quantification has always been of research interest as it is an important step in assessing the treatment efficacy of port-wine stains (PWS) birthmarks. The objective of this study was to investigate the potential application of cellphone camera combined with manual labeling for area quantification. Materials and Methods: First, the color of the untreated PWS lesions was measured and RGB values extracted. Then, a total 48 PWS patches to represent different color, shape and size were printed. Each PWS patch was placed on a 3D head model along with a 1 cm × 1 cm size reference for photographing using a cellphone camera. The lesion region and the small white square region were determined by manual labeling method. The number of pixels within the lesion region and the small white square region were counted and the area of the PWS lesion was calculated. In addition, the image assessment was also compared with the traditional grid counting methods. Results: Each PWS patches were photographed three times. The results showed that the average error rate of the proposed digital photo scheme for the area quantization was 0.132±0.055 and the average error rate of the grid counting scheme for the area quantization was 0.004±0.005. Conclusions: Preliminary study demonstrated that the use of cellphone photo scheme to quantify the area of PWS was feasibility but its error rate was higher than that of the grid method.
Dermoscopy is a useful tool for observing the vascular profile of port-wine stain (PWS) birthmarks. However, due to the complicity of the vascular profile, there is a lack of consensus on the classification of dermoscopic features of PWS vessels. This study investigated the potentials of deep learning-assisted methods in the classification of dermoscopy image-based of PWS vascular profiles. The classified images were used as training samples, and the RegNet network with better classification effect was selected to establish the migration learning method. The results showed that the accuracy of the RegNet network on the validation set was 82.63%. The preliminary study suggests that deep learning assisted PWS vascular contour type classification is feasible.
In situ quantification of photosensitizer is critical in photodynamic therapy (PDT) and photodiagnosis (PD). Fluorescence detection is a feasible approach for the quantification of fluorescent photosensitizer. However, due to the interference of tissue absorption and scattering on the fluorescence spectrum of photosensitizer, it is still challenging to perform in situ fluorescence quantification. In this preliminary study, a Monte Carlo (MC)-based method was used to simulate the fluorescence spectrum and diffuse reflection spectrum of different biological tissues. A calibration algorithm was developed for the correction of the influence of tissue absorption and scattering on protoporphyrin IX (PpIX) fluorescence. Under the excitation of blue light of 405 nm the dispersion coefficient of the original PpIX fluorescence spectrum of the soft tissue phantoms was 28%, which was reduced to 3% after the correction using the calibration algorithm. Under the excitation of red light of 635 nm, the dispersion coefficient of the original PpIX fluorescence spectrum of the soft tissue phantoms was 25%, which was reduced to 1.5% after the correction using the calibration algorithm. The results show that the MC-based method can effectively improve the accuracy of PpIX fluorescence measurement.
Optical coherence tomography (OCT) technology can be used to obtain high resolution cross sectional image of living biological tissues. Early study suggested that some tissue optical properties could also be measured using OCT. In this study, OCT was used to measure the total attenuation coefficient of living kidney of rat and dog models. Results suggested that the total attenuation coefficient of the superficial cortex layer could by derived from a single scattering model. The total attenuation coefficient could be affected by ischemia.
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