L_WT 0 (r), 0 (g), 255 (b) L_rth3 An improved method for the segmentation of roots from X-Ray computed tomography 3D images : Rootine v.2 1 Maxime Phalempin, 1 Eva Lippold, 1 Doris Vetterlein, 1 Steffen Schlüter 1 Department of Soil System Sciences, Helmholtz-Centre for Environmental Research - UFZ, Halle, Germany. X-ray computed tomography (CT) is a powerful tool for the study of root system architecture (RSA) of plants grown in opaque soil (Fig. 1). The study of RSA is however only possible after performing root segmentation, i.e. the binarization of all root and background voxels. The objectives of this work are to develop a segmentation algorithm for which : ❶ the parameters are related to root properties (i.e. the root grey value and diameter); ❷ the number of parameters involved is reduced; ❸ the root recovery rate is higher; ❹ the segmented root diameters are better captured. The ability of the new algorithm to fulfill these criteria will be evaluated by comparison with the former algorithm Rootine (here referred to as “Rootine v.1”) on the benchmark dataset of the so called “worst case” scenario described in Gao et al. (2019). Results Methodology Introduction & motivation Discussions and conclusions References : 1 - Gao, Wei, et al. "A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine." Plant and Soil (2019): 1-13. This project was carried out in the framework of the priority programme 2089 “Rhizosphere spatiotemporal organization - a key to rhizosphere functions” funded by DFG (project number 403640293) y = 0.30x R² = 0.76 y = 0.53x R² = 0.77 0 500 1000 1500 0 500 1000 1500 Root length - X-ray CT (cm) Root length - WinRhizo (cm) Rootine v.1 Rootine v.2 1:1 line Fig. 3 Synoptic view of the Rootine v.2 algorithm Fig. 1 Example of RSA study performed via CT images analysis. Fig. 1 shows possible RSA studies by CT image analysis after performing root segmentation and registration of images obtained after 7, 14 and 21 days after sowing (DAS) of a Zea mays plant. Fig. 4 Root recovery rate (a) and diameter evaluation (b) of Rootine v.2 as compared to v.1 and WinRhizo data 7 DAS 14 DAS 21 DAS Fig. 2 Automatic calculations of the sigma values to be used during Gaussian smoothing Two keysteps were added to the Rootine v.1 workflow (Fig. 3 ). The first one allows to enhance the contrast between the roots and the back ground whereas the second performs an automatic calculations of the sigma values to be used during the Gaussian smoothing with “Tubeness filter”. The latter was established by analyzing the grey value transect of a root of a diameter d r filtered with a sigma value σ (Fig. 2). We introduce a parameter q in order to normalize the results (i.e. q = σ/d r , expressed in number of voxels). Setting q =0.5, retrieving the corresponding grey value (i.e. red dot on Fig. 2) and taking the scaling factor (f s ) into account, we derive : q = 0.12 q = 0.25 q = 0.5 q = 0.75 q = 1 q = 1.5 = d r σ i =d r,i ∗f s ∗ 0.5 On the “worse case” benchmark dataset of Gao et al. (2019), Rootine v.2 … ❶ Performed better than Rootine v.1 in terms of root recovery (Fig. 4a) and diameter evaluation (Fig. 4b); ❷ Involves less parameters to be calibrated by the user thanks to the addition of formalising steps (7 vs. 13 in Rootine v.1); ❸ Involves parameters which are are directly related to root properties. This facilitates the interpretation of parameters and their effects for non-expert users. b a Rootine v.1 Rootine v.2 WinRhizo 0 50 100 150 200 250 Grey value Position along root diameter axis Root diameter [voxels]

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L_WT 0 (r), 0 (g), 255 (b) L_rth3

An improved method for the segmentation of roots from X-Ray computed tomography 3D images : Rootine v.2

1Maxime Phalempin, 1Eva Lippold, 1Doris Vetterlein, 1Steffen Schlüter 1Department of Soil System Sciences, Helmholtz-Centre for Environmental Research - UFZ, Halle, Germany.

X-ray computed tomography (CT) is a powerful tool for the study of root system architecture (RSA) of plants grown in opaque soil (Fig. 1). The study of RSA is however only possible after performing root segmentation, i.e. the binarization of all root and background voxels. The objectives of this work are to develop a segmentation algorithm for which : ❶ the parameters are related to root properties (i.e. the root grey value and diameter); ❷ the number of parameters involved is reduced; ❸ the root recovery rate is higher; ❹ the segmented root diameters are better captured. The ability of the new algorithm to fulfill these criteria will be evaluated by comparison with the former algorithm Rootine (here referred to as “Rootine v.1”) on the benchmark dataset of the so called “worst case” scenario described in Gao et al. (2019).

Results

Methodology

Introduction & motivation

Discussions and conclusions

References : 1 - Gao, Wei, et al. "A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine." Plant and Soil (2019): 1-13.

This project was carried out in the framework of the priority programme 2089 “Rhizosphere spatiotemporal organization - a key to rhizosphere functions” funded by DFG (project number 403640293)

y = 0.30x R² = 0.76

y = 0.53x R² = 0.77

500

1000

1500

0 500 1000 1500

len

gth

- X

-ray

(cm

)

Root length - WinRhizo (cm)

Rootine v.1 Rootine v.2 1:1 line

Fig. 3 Synoptic view of the Rootine v.2 algorithm

Fig. 1 Example of RSA study performed via CT images analysis.

Fig. 1 shows possible RSA studies by CT image analysis after performing root segmentation and registration of images obtained after 7, 14 and 21 days after sowing (DAS) of a Zea mays plant.

Fig. 4 Root recovery rate (a) and diameter evaluation (b) of Rootine v.2 as compared to v.1 and WinRhizo data

7 DAS

14 DAS

21 DAS

Fig. 2 Automatic calculations of the sigma values to be used during Gaussian smoothing

Two keysteps were added to the Rootine v.1 workflow (Fig. 3 ). The first one allows to enhance the contrast between the roots and the back ground whereas the second performs an automatic calculations of the sigma values to be used during the Gaussian smoothing with “Tubeness filter”. The latter was established by analyzing the grey value transect of a root of a diameter dr filtered with a sigma value σ (Fig. 2). We introduce a parameter q in order to normalize the results (i.e. q = σ/dr , expressed in number of voxels). Setting q =0.5, retrieving the corresponding grey value (i.e. red dot on Fig. 2) and taking the scaling factor (fs) into account, we derive :

q = 0.12 q = 0.25 q = 0.5 q = 0.75 q = 1 q = 1.5

= d

σi = dr,i ∗ fs ∗ 0.5

On the “worse case” benchmark dataset of Gao et al. (2019), Rootine v.2 …

❶ Performed better than Rootine v.1 in terms of root recovery (Fig. 4a) and diameter evaluation (Fig. 4b);

❷ Involves less parameters to be calibrated by the user thanks to the addition of formalising steps (7 vs. 13 in Rootine v.1);

❸ Involves parameters which are are directly related to root properties. This facilitates the interpretation of parameters and their effects for non-expert users.

b a

Rootine v.1

Rootine v.2

WinRhizo

100

150

200

250

Gre

y va

lue

Position along root diameter axis