000			nab a22 7a 4500
999			_c64128 _d64120
001			64128
003			MX-TxCIM
005			20210902154526.0
008			200320s2021 xxk|||p|op||| 00| 0 eng d
022			_a1746-4811
024	8		_ahttps://doi.org/10.1186/s13007-021-00735-4
040			_aMX-TxCIM
041			_aeng
100	1		_922448 _aPhalempin, M.
245	1	3	_aAn improved method for the segmentation of roots from X-ray computed tomography 3D images : _brootine v.2
260			_aLondon (United Kingdom) : _bBioMed Central, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aBackground: X-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm “Rootine” and present its succeeding version “Rootine v.2”. In addition to gray value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (i) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (ii) a calculation of the root average gray value based on a histogram analysis, (iii) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (iv) a false negatives recovery based on shape criteria to increase root recovery. We compare the segmentation results of Rootine v.1 and Rootine v.2 with the results of root washing and subsequent analysis with WinRhizo. We use a benchmark dataset of maize roots (Zea mays L. cv. B73) grown in repacked soil for two scenarios with differing soil heterogeneity and image quality. Results: We demonstrate that Rootine v.2 outperforms its preceding version in terms of root recovery and enables to match better the root diameter distribution data obtained with root washing. Despite a longer processing time, Rootine v.2 comprises less user-defined parameters and shows an overall greater usability. Conclusion: The proposed method facilitates higher root detection accuracy than its predecessor and has the potential for improving high-throughput root phenotyping procedures based on X-ray computed tomography data analysis.
546			_aText in English
650		7	_2AGROVOC _91755 _aRoots
650		7	_2AGROVOC _96509 _aImage analysis
650		7	_2AGROVOC _911634 _aRoot systems
650		7	_2AGROVOC _99574 _aX-ray spectroscopy
700	1		_922449 _aLippold, E.
700	1		_922450 _aVetterlein, D.
700	1		_922451 _aSchlüter, S.
773	0		_dLondon (United Kingdom) : BioMed Central, 2021. _gv. 17, art. 39 _tPlant Methods _w57210 _x1746-4811
856	4		_yClick here to access online _uhttps://doi.org/10.1186/s13007-021-00735-4
942			_2ddc _cJA _n0