4.5 Article

On the evaluation of methods for the recovery of plant root systems from X-ray computed tomography images

期刊

FUNCTIONAL PLANT BIOLOGY
卷 42, 期 5, 页码 460-470

出版社

CSIRO PUBLISHING
DOI: 10.1071/FP14071

关键词

root architecture; root image analysis; segmentation

资金

  1. Biotechnology and Biological Sciences Research Council (BBSRC)
  2. Engineering and Physical Sciences Research Council Centre for Integrative Systems Biology program funding
  3. European Research Council
  4. BBSRC
  5. Belgian Science Policy Office [IAP7/29]
  6. Royal Society Wolfson Research Merit Award
  7. EU project EURoot: Enhancing Resource Uptake from Roots under Stress in Cereal Crops [FP7-KBBE-2011-5]
  8. BBSRC [BB/L026848/1, BB/G023972/1] Funding Source: UKRI
  9. Biotechnology and Biological Sciences Research Council [BB/L026848/1, BB/G023972/1] Funding Source: researchfish

向作者/读者索取更多资源

X-ray microcomputed tomography (mu CT) allows nondestructive visualisation of plant root systems within their soil environment and thus offers an alternative to the commonly used destructive methodologies for the examination of plant roots and their interaction with the surrounding soil. Various methods for the recovery of root system information from X-ray computed tomography (CT) image data have been presented in the literature. Detailed, ideally quantitative, evaluation is essential, in order to determine the accuracy and limitations of the proposed methods, and to allow potential users to make informed choices among them. This, however, is a complicated task. Three-dimensional ground truth data are expensive to produce and the complexity of X-ray CT data means that manually generated ground truth may not be definitive. Similarly, artificially generated data are not entirely representative of real samples. The aims of this work are to raise awareness of the evaluation problem and to propose experimental approaches that allow the performance of root extraction methods to be assessed, ultimately improving the techniques available. To illustrate the issues, tests are conducted using both artificially generated images and real data samples.

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