On the evaluation of methods for the recovery of plant root systems from X-ray computed tomography images
Stefan Mairhofer A C D , Craig Sturrock A B , Darren M. Wells A B , Malcolm J. Bennett A B , Sacha J. Mooney A B and Tony P. Pridmore A C
+ Author Affiliations
- Author Affiliations
A Centre for Plant Integrative Biology, University of Nottingham, Nottingham LE12 5RD, UK.
B School of Biosciences, University of Nottingham, Nottingham LE12 5RD, UK.
C School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK.
D Corresponding author. Email: stefan.mairhofer@nottingham.ac.uk
This paper originates from a presentation at the Second International Workshop on Image Analysis Methods for Plant Science, University of Nottingham, 2–3 September 2013.
Functional Plant Biology 42(5) 460-470 https://doi.org/10.1071/FP14071
Submitted: 1 March 2014 Accepted: 1 October 2014 Published: 2 December 2014
Abstract
X-ray microcomputed tomography (μCT) 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.
Additional keywords: root architecture, root image analysis, segmentation.
References
Arsenault J-L, Pouleur S, Messier C, Guay R (1995) WinRHIZO™, a root-measuring system with a unique overlap correction method. HortScience 30, 906Bouix S, Martin-Fernandez M, Ungar L, Nakamura M, Koo MS, McCarley RW, Shenton ME (2007) On evaluating brain tissue classifiers without a ground truth. NeuroImage 36, 1207–1224.
| On evaluating brain tissue classifiers without a ground truth.Crossref | GoogleScholarGoogle Scholar | 17532646PubMed |
Bouma TJ, Nielsen KL, Koutstaal B (2000) Sample preparation and scanning protocol for computerised analysis of root length and diameter. Plant and Soil 218, 185–196.
| Sample preparation and scanning protocol for computerised analysis of root length and diameter.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3cXhvVGrtb8%3D&md5=fa73cc16b1449ef0c4c3fb21b0a65f57CAS |
Heeraman DA, Hopmans JW, Clausnitzer V (1997) Three dimensional imaging of plant roots in situ with X-ray computed tomography. Plant and Soil 189, 167–179.
| Three dimensional imaging of plant roots in situ with X-ray computed tomography.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2sXksVymur0%3D&md5=afbd475bd5141ae9df81077339666649CAS |
Heimann T, Van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman P, Ying Chi , Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmuller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Jeongjin Lee , Lennon B, Rui Li , Senhu Li, Meinzer H-P, Nemeth G, Raicu DS, Rau A-M, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Transactions on Medical Imaging 28, 1251–1265.
| Comparison and evaluation of methods for liver segmentation from CT datasets.Crossref | GoogleScholarGoogle Scholar | 19211338PubMed |
Herman GT (2009) ‘Fundamentals of computerized tomography: image reconstruction from projections.’ 2nd edn. (Springer Publishing Company: London)
Himmelbauer ML, Loiskandl W, Kastanek F (2004) Estimating length, average diameter and surface area of roots using two different image analyses systems. Plant and Soil 260, 111–120.
| Estimating length, average diameter and surface area of roots using two different image analyses systems.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXksFGltLg%3D&md5=e8935a56c4572314572ce890e98317a9CAS |
Iyer-Pascuzzi AS, Symonova O, Mileyko Y, Hao Y, Belcher H, Harer J, Weitz JS, Benfey PN (2010) Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiology 152, 1148–1157.
| Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXmsF2ls78%3D&md5=ed6d363c7e3b010ff872e544d850670eCAS | 20107024PubMed |
Kaestner A, Schneebeli M, Graf F (2006) Visualizing three dimensional root networks using computed tomography. Geoderma 136, 459–469.
| Visualizing three dimensional root networks using computed tomography.Crossref | GoogleScholarGoogle Scholar |
Kak AC, Slaney M (1988) ‘Principles of computerized tomographic imaging.’ (IEEE Press: New York).
Kohlberger T, Singh V, Alvino C, Bahlmann C, Grady L (2012) Evaluating segmentation error without ground truth. In ‘Medical Image Computing and Computer-Assisted Intervention – MICCAI Proceedings,’ 1–5 October 2012, Nice, France. (Eds N Ayache, H Delingette, P Golland, K Mori) pp. 528–536.
Lontoc-Roy M, Dutilleul P, Prasher SO, Han L, Brouillet T, Smith DL (2006) Advances in the acquisition and analysis of CT scan data to isolate a crop root system from the soil medium and quantify root system complexity in 3-D space. Geoderma 137, 231–241.
| Advances in the acquisition and analysis of CT scan data to isolate a crop root system from the soil medium and quantify root system complexity in 3-D space.Crossref | GoogleScholarGoogle Scholar |
Lynch JP, Nielsen KL, Davis RD, Jablokow AG (1997) Simroot: modelling and visualization of root systems. Plant and Soil 188, 139–151.
| Simroot: modelling and visualization of root systems.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2sXjvFymsrc%3D&md5=d0c7e7be5f88b99d01fdae5bf2108fbbCAS |
Mairhofer S, Zappala S, Tracy SR, Sturrock C, Bennett MJ, Mooney SJ, Pridmore TP (2012) RooTrak: automated recovery of three-dimensional plant root architecture in soil from X-ray micro computed tomography images using visual tracking. Plant Physiology 158, 561–569.
| RooTrak: automated recovery of three-dimensional plant root architecture in soil from X-ray micro computed tomography images using visual tracking.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XltVOkt78%3D&md5=3bad81fc2de2894603ad936b1e59fb8eCAS | 22190339PubMed |
Mairhofer S, Zappala S, Tracy SR, Sturrock C, Bennett MJ, Mooney SJ, Pridmore TP (2013) Recovering complete plant root system architectures from soil via X-ray µ-computed tomography. Plant Methods 9, 8
| Recovering complete plant root system architectures from soil via X-ray µ-computed tomography.Crossref | GoogleScholarGoogle Scholar | 23514198PubMed |
Martin D, Fowlkes C, Tal D, Malik J (2011) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ‘Computer Vision 2001 Proceedings. Eighth IEEE International Conference, 7–14 July’. Vancouver, BC, Canada. Vol. 2. (Ed. B Werner) pp. 416–423. (IEEE Computer Society: Los Alamitos, California)
O’Callaghan J, Wells J, Richardson S, Holmes H, Yu Y, Walker-Samuel S, Siow B, Lythgoe MF (2014) Is your system calibrated? MRI gradient system calibration for pre-clinical, high-resolution imaging. PLoS ONE 9, e96568
| Is your system calibrated? MRI gradient system calibration for pre-clinical, high-resolution imaging.Crossref | GoogleScholarGoogle Scholar | 24804737PubMed |
Perret JS, Al-Belushi ME, Deadman M (2007) Non-destructive visualization and quantification of roots using computed tomography. Soil Biology & Biochemistry 39, 391–399.
| Non-destructive visualization and quantification of roots using computed tomography.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xht1KrsbrL&md5=5ef8f909feb887d3a12b5dc531ac76bfCAS |
Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1090–1104.
Pierret A, Capowiez Y, Moran CJ, Kretzschmar A (1999) X-ray computed tomography to quantify tree rooting spatial distributions. Geoderma 90, 307–326.
| X-ray computed tomography to quantify tree rooting spatial distributions.Crossref | GoogleScholarGoogle Scholar |
Sethian JA (1999) ‘Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science.’ Cambridge monograph on applied and computational mathematics. 2nd edition. (Cambridge University Press: Cambridge, UK)
Toft P (1996) The radon transform – theory and implementation. PhD thesis, Technical University of Denmark, Kongens Lyngby, Denmark.
Tracy SR, Roberts JA, Black CR, McNeill A, Davidson R, Mooney SJ (2010) The X-factor: visualizing undisturbed root architecture in soils using X-ray computed tomography. Journal of Experimental Botany 61, 311–313.
| The X-factor: visualizing undisturbed root architecture in soils using X-ray computed tomography.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXktlGmug%3D%3D&md5=629e561329dd5c70e4b2581744df4f6eCAS | 20051353PubMed |
Yan Y, Rosales R, Fung G, Schmidt MW, Valadez GH, Bogoni L, Moy L, Dy JG (2010) Modeling annotator expertise: learning when everybody knows a bit of something. In ‘International conference on artificial intelligence and statistics proceedings,’ 13–15 May 2010, Sardinia, Italy. (Eds YW Teh, M Titterington) pp. 932–939. (Microtome Publishing: Brookline, MA, USA)
Zappala S, Helliwell JR, Tracy SR, Mairhofer S, Sturrock CJ, Pridmore T, Bennett M, Mooney SJ (2013) Effects of X-ray dose on rhizosphere studies using X-ray computed tomography. PLoS ONE 8, e67250
| Effects of X-ray dose on rhizosphere studies using X-ray computed tomography.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtFWitrvL&md5=c3ff0b509a3cafc1bbb2ce7be488fca5CAS | 23840640PubMed |
