79 research outputs found
Towards robust and effective shape prior modeling: sparse shape composition
No full textOrgan shape plays an important role in many clinical practices, including diagnosis, surgical planning and treatment evaluation. It is usually derived from medical images using low level appearance cues. However, due to diseases and imaging artifacts, low level appearance cues are often weak or misleading. In this situation, shape priors become critical to infer and refine the shape derived from image appearances. Effective modeling of shape priors is challenging because: 1) shape variations are complex and cannot always be modeled by parametric probability distributions; 2) a shape instance derived from image appearance cues (called an input shape) may have significant errors; and 3) local details of an input shape may be important for clinical purposes but difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to address these three challenges in a unified framework. With our method, a sparse set of shapes is selected from the shape repository and composed together to infer and refine an input shape. This way, the prior information is implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: 1) the input shape can be approximately represented by a sparse linear combination of shapes in the shape repository; 2) parts of the input shape may contain large errors but such errors are sparse. Our model is formulated as a sparse learning problem. Using norm relaxation, it can be solved by an efficient expectation-maximization (EM) framework. Furthermore, this model is extended to effectively handle multi-resolution, local shape priors and hierarchical priors. We also propose a framework to generate high quality training data in 3D. Our framework includes geometry processing methods and shape registration algorithms. The proposed shape prior model is extensively validated on five different medical applications: 2D lung localization in chest X-ray images, 3D liver segmentation in low-dose Computed Tomography (CT) scans, 3D segmentation of multiple rodent brain structures in Magnetic Resonance (MR) microscope, real time tracking of left ventricles in Magnetic Resonance Imaging (MRI), and high resolution CT reconstruction. Compared to state-of-the-art methods, our model exhibits better performance in all these studies.Ph. D.Includes bibliographical referencesIncludes vitaby Shaoting Zhan
Reflecting on Experiences of Senior Medical Students’ External Clinical Teaching Visits in General Practice Placements: A Pilot Study [Response to Letter]
No full textShaoting Feng,1,* Daya Yang,1,* Kunsong Zhang,1,* Denise Joy Findlay,2 Ming Kuang,1 Haipeng Xiao,2 Dan Xu1,2 1Department of Medical Education, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2General Practice Research, Curtin Medical School, Faculty of Health Sciences, Curtin University, Perth, Australia*These authors contributed equally to this workCorrespondence: Dan Xu; Ming Kuang, Email [email protected]; [email protected]
Interannual and seasonal variability of glacier surface velocity in the parlung zangbo basin, tibetan plateau
No full textMonitoring glacier flow is vital to understand the response of mountain glaciers to environmental forcing in the context of global climate change. Seasonal and interannual variability of surface velocity in the temperate glaciers of the Parlung Zangbo Basin (PZB) has attracted significant attention. Detailed patterns in glacier surface velocity and its seasonal variability in the PZB are still uncertain, however. We utilized Landsat-8 (L8) OLI data to investigate in detail the variability of glacier velocity in the PZB by applying the normalized image cross-correlation method. On the basis of satellite images acquired from 2013 to 2020, we present a map of time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu, and Azha) in the PZB. Next, we explored the driving factors of surface velocity and of its variability. The results show that the glacier centerline velocity increased slightly in 2017–2020. The analysis of meteorological data at two weather stations on the outskirts of the glacier area provided some indications of increased precipitation during winter-spring. Such increase likely had an impact on ice mass accumulation in the up-stream portion of the glacier. The accumulated ice mass could have caused seasonal velocity changes in response to mass imbalance during 2017–2020. Besides, there was a clear winter-spring speedup of 40% in the upper glacier region, while a summer speedup occurred at the glacier tongue. The seasonal and interannual velocity variability was captured by the transverse velocity profiles in the four selected glaciers. The observed spatial pattern and seasonal variability in glacier surface velocity suggests that the winter-spring snow might be a driver of glacier flow in the central and upper portions of glaciers. Furthermore, the variations in glacier surface velocity are likely related to topographic setting and basal slip caused by the percolation of rainfall. The findings on glacier velocity suggest that the transfer of winter-spring accumulated ice triggered by mass conservation seems to be the main driver of changes in glacier velocity. The reasons that influence the seasonal surface velocity change need further investigation.Optical and Laser Remote Sensin
Changes in glacier albedo and the driving factors in the Western Nyainqentanglha Mountains from 2001 to 2020
No full textGlacier surface albedo dominates glacier energy balance, thus strongly affecting the glacier mass balance. Glaciers in the Western Nyainqentanglha Mountains (WNM) experienced large mass losses in the past two decades, but long-term changes of glacier albedo and its drivers are less understood. In this study, we retrieved glacier albedo with MODIS reflectance data to characterize the spatiotemporal variability of albedo from 2001 to 2020. Air temperature, rainfall, snowfall and deposition of light-absorbing impurities (LAIs) were evaluated as potential drivers of the observed variability in glacier albedo. The results showed that: (1) the glacier albedo experienced large inter-annual fluctuations, with the mean albedo being 0.552 ± 0.002 and a clear decreasing trend of 0.0443 ± 2 × 10-4 dec-1 in the WNM. The fastest decline was observed in autumn and in the vicinity of the equilibrium line altitude, indicating an extended melt season and an expansion of the ablation region to higher elevation; (2) local meteorology and LAIs deposition are the main drivers of glacier albedo change, but their effects on seasonal albedos are different due to different glacier processes. Both air temperature and the balance between liquid and solid precipitation affect summer and autumn albedos due to glacier ablation. Air temperature is the main driver of spring and winter albedos due to sublimation and metamorphism of snow, while snowfall carried by westerlies has limited influence on these two seasonal albedos due to less snowfall. LAIs mainly affect spring albedo due to high concentration coupled with the southerly wind in spring. These findings highlight the significance of changes in glacier albedo and the key role of local meteorology and LAIs deposition in determining such changes, which play an important role in glaciological and cryosphere processes. Optical and Laser Remote Sensin
Glacier area and snow cover changes in the range system surrounding tarim from 2000 to 2020 using google earth engine
No full textGlacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier.Optical and Laser Remote Sensin
A new segmentation framework based on sparse shape composition in liver surgery planning system
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