[Ecological risk assessment of landslide disasters based on potential loss of ecosystem services]. / 基于生态系统服务潜在损失的滑坡灾害生态风险评价.

Landslides are common geological calamities in mountainous regions, which not only threaten social and economic development and residents' safety but also cause ecosystem damage, with consequences on human welfare. A more comprehensive and systematic reference for disaster prevention and mitigation with ecosystem services loss as an index for the potential damage of ecosystem could aid the progress of landslide ecological risk assessment. Five provinces in Southwest China (Sichuan, Yunnan, Guizhou, Guangxi and Chongqing) have diverse landforms, complex stratum and lithology, and active geological tectonic movements, which are the most landslide prone areas in China. In this study, the ecological risk assessment framework, model and indicator were constructed from three dimensions, including disaster risk, vulnerability and potential loss of the ecosystem. Disaster risk was based on the comprehensive analysis of factors such as geology, topo-graphy, landform, precipitation, and their mutual relationship. The vulnerability of the ecosystem was characterized by landscape patterns indices. The potential loss was measured by ecosystem service to evaluate the ecological risk associated with landslide hazards in the five provinces of Southwest China. The results showed that the areas with high potential loss of ecosystem services were mainly distributed in the south of Ailao Mountain in Yunnan Province, Qionglai Mountain of Sichuan Pro-vince, Hengduan Mountains, Dadu River Basin, Northwest Guangxi Autonomous Region, and eastern area of Dayao Mountain. The high ecological risk of landslide hazard in the study area mainly distributed in the areas of Min Mountain, Qionglai Mountain, Wuliang Mountain, Ailao Mountain, Miao Ridge, Leigong Mountain, and Dadu River basin. With respect to altitude, 500-1500 m was the main high-risk areas, accounting for 37.9% of the high-risk area. In terms of ecosystem types, forests are the high-risk areas, accounting for 66.4% of the high-risk areas. Landslide monitoring and early warning in the high ecological risk areas should be strengthened, through strengthening ecosystem protection in the region and improving the stability and resistance of ecosystems.

Left ventricle segmentation in dynamic cardiac CT using random walks method.

PURPOSE: Segmentation of the left ventricle (LV) in cardiac CT (CCT) images is difficult due to the intensity heterogeneity arising from accumulation of contrast agent in papillary muscle and trabeculae carneae. In this study, we demonstrated the random walks method for LV segmentation in CCT through cardiac phases. METHODS: 63 CCT data sets from 7 patients with 9 cardiac phases were included in this study. All cardiac CT examinations were performed with GE 64-detector CT scanner with ECG gating. In each patient, 60-80 ml iohexol was injected at a flow rate of 5 ml/sec followed by 60 ml normal saline solution. Random walks (RW) based on probability of labels was used for LV segmentation. The LV delineations generated by the experienced physician (MD), conventional image-based method (IB), and RW were compared. RESULTS: In general the contours segment the LV closely by RW and MD, but the discrepancies in papillary muscle and trabeculae carneae were observed while using the IB method. CONCLUSION: We showed the RW method potentially improved LV segmentation as compared to the volume by conventional IB method. In this study, we demonstrated the clinical feasibility of LV volume segmentation using random walks algorithm.