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Sökning: WFRF:(Guillard Gwenael)

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1.
  • Grasset, Charlotte, et al. (författare)
  • Carbon emission along a eutrophication gradient in temperate riverine wetlands : effect of primary productivity and plant community composition
  • 2016
  • Ingår i: Freshwater Biology. - : Wiley. - 0046-5070 .- 1365-2427. ; 61, s. 1405-1420
  • Tidskriftsartikel (refereegranskat)abstract
    • Eutrophication increases primary productivity and favours the predominance of floating vegetation in wetlands. Carbon (C) fluxes in wetlands are strongly driven by primary productivity and can differ by vegetation type. However, to the best of our knowledge, the role of eutrophication in C fluxes has rarely been assessed. Consequently, we aimed to measure the seasonal variation in carbon dioxide (CO2) and methane (CH4) fluxes at six aquatic sites in four temperate wetlands, ranging along a gradient of sediment total phosphorus content, and determine whether C fluxes correlate with above-ground net primary productivity (ANPP) and plant community composition along this eutrophication gradient. Daytime CO2 emissions were significantly and negatively correlated with wetland net primary productivity as a result of the greater C fixation by photosynthesis during the peak of production. Conversely, CH4 emissions were significantly and positively correlated with wetland ANPP, possibly due to higher litter production and anaerobic decomposition. The highest CH4 emissions were observed above floating vegetation, which favoured hypoxic conditions in the water column. CH4 emissions including ebullition were higher above macroalgal belts than above vascular plants with floating leaves. CH4 emissions without ebullition (i.e. resulting from plant transport and diffusion) better correlated with the abundance of macroalgae than with the abundance of vascular plants with floating leaves. Our results suggest that eutrophication may greatly modify CO2 and CH4 emissions from wetlands through changes in vegetation type and productivity.
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2.
  • Litjens, Geert, et al. (författare)
  • Evaluation of prostate segmentation algorithms for MRI : The PROMISE12 challenge
  • 2014
  • Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 18:2, s. 359-373
  • Tidskriftsartikel (refereegranskat)abstract
    • Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p < 0.05) and had an efficient implementation with a run time of 8 min and 3 s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/. (C) 2013 Elsevier B.V. All rights reserved.
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