A Brief Review on the In Situ Synthesis of Boron-Doped Diamond Thin Films

Diamond thin films are well known for their unsurpassed physical and chemical properties. In the recent past, research interests in the synthesis of conductive diamond thin films, especially the boron-doped diamond (BDD) thin films, have risen up to cater to the requirements of electronic, biosensoric, and electrochemical applications. BDD thin films are obtained by substituting some of the sp3 hybridized carbon atoms in the diamond lattice with boron atoms. Depending on diamond thin film synthesis conditions, boron doping routes, and further processing steps (if any), different types of BDD diamond thin films with application-specific properties can be obtained. This paper will review several important advances in the synthesis of boron-doped diamond thin films, especially those synthesized via gas phase manipulation

Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study

Abstract We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2− (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR). GCGMM segmentations were significantly more accurate than GrowCut (GC) and fuzzy c-means clustering (FCM). GCGMM’s segmentations and the texture features computed from those segmentations were the most reproducible compared with manual delineations and other analyzed segmentation methods. Finally, random forest (RF) classifier trained with leave-one-out cross-validation using features extracted from GCGMM segmentation resulted in the best accuracy for ER-HER2+ vs. ERPR+/TN (GCGMM 0.95, expert 0.95, GC 0.90, FCM 0.92) and for ERPR + HER2− vs. TN (GCGMM 0.92, expert 0.91, GC 0.77, FCM 0.83)