Table 1 Steps in Root Hair Sizer (RHS) algorithm

0

Requirement: one channel focused .tif images

Sum projection of a Z-stack containing five slices

1^a

Definition of custom measurement settings for the analysis:

Dialog boxes interface

  Position along the root axis where to start the analysis

0 µm

  Position along the root axis where to end the analysis

10,000 µm

  Width of the rectangular selection used to measure RH length

8 µm

  Interval for recovery of maximal RH size values

2, 10, 50 or 250

  Facultative step: give a name to the different class of annotations that the user could do manually in step 9.1 (e.g. Artefact, RHs in one side of the root shorter than the other…)

  Input folder definition for batch image analysis

  Output folder definition

Following steps are executed for each .tif image in input folder

2

Modification of image type to 8-bit

3

Generation of a binary image of the root with its RHs

 3.1^b

  Thresholding of step 2 image

Auto Local Threshold—Bernsen method, black background

Radius: 5

 3.2

  Shape smoothing

  3.2.1^b

    Dilatation and erosion of step 3.1 image

Dilate and Erode

5 times

  3.2.2

    Holes filling in image 3.2.1

Fill Holes

 3.3

  Storage as ROI

Analyze Particles—particles size: 0 to infinite pixels and ROI Manager

4

Generation of a binary image for the root body alone (without RHs)

 4.1^b

  Thresholding of step 2 image

Auto Local Threshold—Phansalkar method, white background

Radius: 100

 4.2

  Shape smoothing

  4.2.1^b

    Dilatation and erosion of step 4.1 image

Dilate and Erode

10 times

  4.2.2

    Holes filling in step 4.2.1 image

File Holes

  4.2.3^b

    Erosion and dilatation of step 4.2.2 image

Erode and Dilate

12 times

 4.3

  Background cleaning of step 4.2.3 image using the inverted selection of the root (with RHs) defined at step 3.3

ROI Manager and Fill

 4.4

  Inversion of step 4.3 image

Invert

 4.5

  Storage as ROI

Analyze Particles—particles size: 0 to infinite pixels and ROI Manager

5

Generation of a binary image for the area covered by RHs alone (without root body)

 5.1

  Subtraction of step 4.4 Image from step 3.2.2 image

Image Calculator

 5.2

  Background cleaning of step 5.1 image using the inverted selection of the root (with RHs) defined at step 3.3

ROI Manager and Fill

 5.3

  Storage as ROI

ROI Manager-XOR

6^a

Manual suppression of obvious thresholding errors remaining in the step 5.2 image^a

Dialog box interface and Brush

7

Storage in a ROI of the RH shape resulting from step 6

Create Selection and Make Inverse

8

Definition of a root axis line (Root median line, RML)

 8.1

  Skeletisation of step 4.4 image

Skeletonize

 8.2

  Recovery of the skeleton longest path

Analyse Skeleton (2D/3D)

 8.3^a,c

  Convert skeleton step 8.2 image into a segmented line^c

  Manual definition of the RT on this line^a

Dialog box interface

 8.4^b

  Simplification of step 8.3 segmented line

1 segment kept every 200

 8.5^a

  RML adjustment

Dialog box interface

 8.6

  Storage as ROI of automated and manually curated RMLs

ROI Manager

9

Definition of artefacts or any other comments on image

 9.1^a

  Rough contouring of the zones to be highlighted

Polygon Selection

 9.2^a

  Naming (using remarks defined at step 1) of the highlighted zones

Dialog box interface

 9.3

  Storage as ROI of rough contours from step 9.1

ROI Manager

 9.4

  Recovery of minimum and maximum positions of the rough contours along RML analysing the triangle ABC defined by one point of rough contour (apex A), and one segment of the RML (apexes B and C). AI is the median of the triangle (emerging from A), with I the intersection between AI and BC. D is the closest point from A on RML. Steps 9.4.1 to 9.4.3 are done for each point of the rough contour

  9.4.1

    Measurement of AI lengths for all RML segments and determination of the shorter one

  9.4.2

    Segmentation pixel by pixel of the closer RML segment and measurement of the distance to the apex A of each pixel. The pixel on RML associated with the shorter distance is called D

  9.4.3

    Measurement of the distance between the RT and D

  9.4.4

    Recovery of minimal and maximal lengths measured at step 9.4.3 for all rough contour points to store them in an array written to the results table

10^b

Straightening of image obtain at step 5.2 using the custom RML from step 8.5 as a guide, with line width large enough to cover the root thickness

Straighten

RML segmented line width: 1500 pixels

11

Step 10 image binarization

Make Binary

12

Division by 255 of pixel intensities in step 11 image

Divide − 255

13

Step 12 image 90° rotation

Rotate

14

RH measurement. This step is done between the positions defined at steps 1, measuring first the left side of the root, then the right side

 14.1

  Creating a selection with width defined at step 1 on one half of the straighten root image (step 10)

 14.2

  Sum intensities of pixels included in the selection (this will give the number of RH pixels) and associate the result with the distance of the selection from the RT

Measure—RawIntDens

 14.3

  Repeat steps 14.1 and 14.2 translating the selection by its width to scan the full length of the root

 14.4

  Using the interval width defined at step 1, recover the local maximum value within values measured at steps 14.2 and its distance from the RT. Proceeds the same search on next interval until all values measured at step 14.3 are analysed.

 14.5

  Convert pixels number recovered at step 14.4 into RH length: the RH pixels area in scanning selection is approximated to a rectangle with known width. The height of this rectangle can be deduced from the rectangle area formula: height = area/width. The length in pixel is then converted to µm

15

Create a chart summarizing initial settings, maximal RH lengths in the defined interval and associated distances from the RT. A binary code indicates for each measurement if an annotation was done at step 9.4.4 (0 = no annotation, 1 = annotation present)

16

Save the chart and the ROI generated all along the process