FRNET: Flattened Residual Network for Infant MRI Skull Stripping

Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.Comment: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI

A Recognition Approach Study on Chinese Field Term Based Mutual Information /Conditional Random Fields

AbstractA new auto-recognition approach based on mutual information/ Conditional Random Fields (CRFs) was put forward in this study. Firstly, statistics-based mutual information algorithm was applied to separate the Chinese words accurately, then the sub-words were picked out from the accurate separation according to the entropy of the left and right information. Secondly, the relative frequency of the sub-words was calculated. Thirdly, three training characteristics, including words, part of speech and relative frequency, were used as training datasets to obtain a model for field terms characters by CRFs. Thirdly, the Chinese words recognition was accomplished by the CRFs model. Finally, a practical experiment was executed and the results showed that the precision, percentage and Fmeasure of the recognition is 78.63%, 87.10% and 82.65% respectively, which is significant better the normal mutual information/ Conditional Random Fields (CRFs) algorithm

Poly[trans-diaquabis­[μ2-2-(pyridin-3-yl)acetato-κ2 N:O]­zinc]

In the title coordination polymer, [Zn(C7H6NO2)2(H2O)2]n, the ZnII cation is located on an inversion center and is coordinated by four pyridyl­acetate anions and two water mol­ecules in a distorted ZnN2O4 octa­hedral geometry. The pyridine-N and carboxyl­ate-O atoms of the pyridyl­acetate anion connect to two ZnII cations, forming a two-dimensional polymeric complex extending parallel to (212). Inter­molecular O—H⋯O and weak C—H⋯O hydrogen bonding is present in the crystal structure