Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.
In this study, the decomposition of methanol into the CO and H species on the Pd/tungsten carbide (WC)(0001) surface is systematically investigated using periodic density functional theory (DFT) calculations. The possible reaction pathways and intermediates are determined. The results reveal that saturated molecules, i.e., methanol and formaldehyde, adsorb weakly on the Pd/ WC(0001) surface. Both CO and H prefer three-fold sites, with adsorption energies of −1.51 and −2.67 eV, respectively. On the other hand, CH3O stably binds at three-fold and bridge sites, with an adsorption energy of −2.58 eV. However, most of the other intermediates tend to adsorb to the surface with the carbon and oxygen atoms in their sp3 and hydroxyl-like configurations, respectively. Hence, the C atom of CH2OH preferentially attaches to the top sites, CHOH and CH2O adsorb at the bridge sites, while COH and CHO occupy the three-fold sites. The DFT calculations indicate that the rupture of the initial C–H bond promotes the decomposition of CH3OH and CH2OH, whereas in the case of CHOH, O–H bond scission is favored over the C–H bond rupture. Thus, the most probable methanol decomposition pathway on the Pd/WC(0001) surface is CH3OH → CH2OH → trans-CHOH → CHO → CO. The present study demonstrates that the synergistic effect of WC (as carrier) and Pd (as catalyst) alters the CH3OH decomposition pathway and reduces the noble metal utilization. 相似文献
