In-Vivo Tape Stripping Study with Caffeine for Comparisons on Body Sites, Age and Washing.

PURPOSE: Assessing the percutaneous absorption of cosmetic ingredients using in-vitro human skin reveals certain limitations, such as restricted anatomical sites and repeated exposure, and to overcome these issues, in-vivo studies are required. The aim of the study is to develop a robust non-invasive in-vivo protocol that should be applicable to a wide range of application. METHODS: A robust tape stripping protocol was therefore designed according to recent recommendations, and the impact of two different washing procedures on caffeine distribution in tape strips was investigated to optimise the protocol. The optimised protocol was then used to study the effect of age and anatomical area on the percutaneous absorption of caffeine, including facial areas which are not readily available for in-vitro studies. RESULTS: With tape stripping, a difference between the percutaneous absorption on the face (forehead, cheek) and the volar forearm was observed. No obvious difference was observed between percutaneous absorption in young and post-menopausal women, but this could be due to the limited number of subjects. CONCLUSION: This tape stripping protocol is now to be deployed to address many other factors, such as percutaneous absorption in other anatomical areas (e.g. abdomen, axilla, etc.), impact of repeated applications and effect of formulation.

[Automatic Segmentation of Anatomical Areas in X-ray Images Based on Fully Convolutional Networks].

OBJECTIVE: Medical image segmentation is a key step in medical image processing. An architecture of fully convolutional networks was proposed to realize automatic segmentation of anatomical areas in X-ray images. METHODS: Enlightened by the advantages of convolutional neural networks on features extraction, fully convolutional networks consisting of 9 layers were designed to segment medical images. The networks used convolution kernels of various sizes to extract multi-dimensional image features in the images, meanwhile, eliminated pooling layers to avoid the loss of image details during downsampling procedures. RESULTS: The experiment was conducted in accordance with the specific scene of X-ray images segmentation. Compared with traditional segmentation methods, this approach achieved more accurate segmentation of anatomical areas. CONCLUSIONS: Fully convolutional networks can extract representative and multidimensional features of medical images, avoid the loss of image details during downsampling procedures, and complete automatic segmentation of anatomical areas accurately in X-ray images.

Automatic Segmentation of Anatomical Areas in X-ray Images Based on Fully Convolutional Networks / 中国医疗器械杂志

OBJECTIVE@#Medical image segmentation is a key step in medical image processing. An architecture of fully convolutional networks was proposed to realize automatic segmentation of anatomical areas in X-ray images.@*METHODS@#Enlightened by the advantages of convolutional neural networks on features extraction, fully convolutional networks consisting of 9 layers were designed to segment medical images. The networks used convolution kernels of various sizes to extract multi-dimensional image features in the images, meanwhile, eliminated pooling layers to avoid the loss of image details during downsampling procedures.@*RESULTS@#The experiment was conducted in accordance with the specific scene of X-ray images segmentation. Compared with traditional segmentation methods, this approach achieved more accurate segmentation of anatomical areas.@*CONCLUSIONS@#Fully convolutional networks can extract representative and multidimensional features of medical images, avoid the loss of image details during downsampling procedures, and complete automatic segmentation of anatomical areas accurately in X-ray images.