From January 2006 to November 2012, 210 patients underwent methyl methacrylate forehead augmentation on an all outpatient basis. Under only local anesthesia, a V-shaped transverse scalp incision (5 cm length) was made behind the YH25448 cell line front hair line, followed by subperiosteal dissection of the skin covering the forehead. Methyl methacrylate was inserted and manually molded to the desired contour through the skin. The amount of methyl methacrylate used ranged from 10

to 40 mL, with a mean of 25 mL, depending on the size and shape. The follow-up period ranged from 3 months to 6 years, averaging 45 months, and with the exception of a very small percentage, all patients were satisfied with the results. Based on these results, the author concludes that aesthetic forehead augmentation using methyl methacrylate is an effective surgical procedure with minimal side effects and a high degree of patient satisfaction.”
“Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis AP26113 support. However, traditional CBIR

systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called “”Dense-Sift,”" that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures Selleckchem Tyrosine Kinase Inhibitor Library that reflect

how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them.