bruker-microct method note: automated trabecular...

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Automated trabecular and

cortical bone selection

Method note

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2 Bruker-MicroCT method note: Automated trabecular and cortical bone selection

Introduction

In practice, quantitative analysis of long bone morphology is subdivided into analysis

of the trabecular bone and analysis of the cortical bone. To do so, one has to

adequately set a region of interest corresponding to either the trabecular bone or the

cortical bone. This is very time-consuming as it has to be done manually for every

individual bone.

The goal of this application note is to demonstrate how this can be done in an

automated way using custom processing, which is much faster and more accurate

than the manual way, increasing reproducibility of the results and lowering variation.

Part 1: Regions of interest (ROI) page

Before opening the dataset in CTAn, make sure the long axis of the bone is aligned

along the Z-axis. If not, one can reorient the dataset using dataviewer and save the

dataset in the right orientation. Upon opening of the dataset in CTAn, browse to the

‘Regions of interest’ page. The first and only manual step is to save 2 new

datasets: a subset of slices in the long bone metaphysis to run the trabecular

analysis (‘VOI trab’), and a subset of slices in the long bone diaphysis to run the

cortical analysis (‘VOI cort’). Typically, one will select the growth plate as a reference

point and define a specific offset and height to select the appropriate region

(described in detail in the method note ‘mouse bone analysis method’). Yet, and in

contrast to what used to be done, no specific ROI has to be created on the slices

themselves to separate the trabecular bone from the cortical bone.

Slice of metapyseal ROI Slice of diapyseal ROI

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Part 2: Custom processing page

Starting from the ROI datasets that have been saved, one used to manually draw a

ROI separating the trabecular bone from the cortical bone in order not to contaminate

the trabecular analysis with (parts of the) cortical bone and the other way around.

However, using custom processing, it is possible to run an automated task list to do

so. First a protocol will be set up to automatically select the trabecular part of the

bone (the bone marrow cavity in principal), and a few more steps will be added to this

protocol in case a cortical selection is required.

A. Trabecular selection

After opening the ‘VOI trab’ dataset, one first has to browse to the binary page to

select an appropriate threshold to segment the bone from the soft tissue and

background. In this case, a threshold of 80 was selected. Once the correct threshold

values are selected, proceed to the ‘Custom processing’ page.

A copy of the dataset will be loaded. Three very important buttons are on the right

side of the plug-ins bar:

image view

image inside ROI view

ROI view

As you have just loaded a copy of the dataset into the custom processing page, the

initial settings are:

Image view Image inside ROI view ROI view

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The idea behind an automated trabecular selection is in fact delineating the bone

marrow cavity, including all trabecular bone and excluding any cortical bone.

Therefore a region of interest has to be selected corresponding to the bone cross-

sectional area (step 1). To do so, a (global) threshold will be applied to adequately

segment the bone tissue from the soft tissue and background.


Other parts of the bone besides the tibia or femur metaphysis or other segmented

structures (such as the fibula or noise speckles) can be included in the binary

selection. These can easily be removed by running a sweep function in 3D removing

all except the largest object (step 2).

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In the next step, a ROI shrink-wrap function is applied in 2D to limit the ROI to the

outside borders of the cortical bone (step 3). The stretch over holes function is

activated to ignore pores running through the cortical bone towards the bone marrow.

If not, one might risk to exclude the bone marrow from the ROI at slices where the

bone marrow is connected to the space outside of the bone through pores in the

cortical bone.

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Once the ROI is set to the outside of the bone, one can proceed with the selection of

the bone marrow cavity. To do so, run a bitwise operation setting ‘image = NOT

image’ to invert the image (step 4).


In the image view, one can see that not only the bone marrow, but also the outside

background is selected by the invert function. Therefore, apply a bitwise operation

‘image = image AND region of interest’ to limit the image to the region of interest and

exclude the outside background from the selection (step 5).

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Due to the ROI shrink wrap stretch over holes function, several white speckles

appear at the periosteal surface of the bone which have to be excluded from the

selection. Several despeckling functions can be applied to do so, although a small

opening procedure is more effective in this specific case (step 6).

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Optionally, remaining white speckles, if any are still present, can be removed by

applying a sweep function in 3D, removing all except the largest object (step7).


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At this point, the bone marrow is selected but the trabecular bone is not included in

this selection and appears as black speckles or gaps inside the bone marrow

selection. These gaps can be removed by applying a closing function in 2D with a

large radius (step 8). Setting the radius too small will not include trabecula in the

selection that have a thickness larger that the closing radius.


Following the closing procedure, the bone marrow cavity including the trabecular

bone is selected. However, at slides showing a pore running through the cortical

bone, the selection might include these pore spaces. To limit the bone marrow cavity

selection to the endosteal surface, an opening function can be applied in 2D with a

rather large radius which will result in removal of these spikes and smoothing of the

bone marrow cavity selection (step 9).

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To make sure the bone marrow cavity selection doesn’t include any parts of thye

cortical bone, a small erosion function is applied, resulting in a bone marrow cavity

selection starting only a few pixels away from the endocortical surface (step 10).

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In order to be able to save this bone marrow cavity selection as a region of interest

delineating the trabecular bone, as well as a new dataset from this selection, one has

to copy the bone marrow cavity selection to the region of interest by running a bitwise

operation ‘region of interest = COPY image’ (step 11).


The second last step of the trabecular selection tasklist involves reloading the image

in orde to be able to save a new dataset from the region of the ROI (step 12).

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The last step is to save a new dataset containing the bone marrow cavity selection

only, as well as the bone marrow cavity selection dataset as a region of interest.

Therefore, save the image inside the region of interest, as well as the region of

interest dataset as a binary selection (step 13).

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Two new folders will be created by default in the ‘VOI trab’ dataset folder, the first

one being the first dataset saved, the second one being the second dataset saved.

The binary ROI dataset can be loaded as a region of interest in the regions of interest

tab in CTan, selecting bitmap files to be loaded instead of a .roi file, either on the

‘VOI trab’ dataset or the newly saved bone marrow cavity selection dataset.

Original 'VOI trab' dataset

with ROI applied

New trabecular selection dataset

with ROI applied

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B. Cortical selection

For the cortical selection, open the ‘VOI cort’ dataset and browse to the custom

processing page. First part of the cortical selection tasklist is basically a

recapitulation of the trabecular selection tasklist. Repeat steps 1 to 10 from the

trabecular selection tasklist (A), to end up with a region of interest corresponding to

the bone cross-sectional area and an image view being the bone marrow cavity

selection.


Selection of the cortical area now involves dilating the region of interest with a few

pixels to be sure all of the cortical bone is included in the region of interest, followed

by substraction of the bone marrow cavity selection to exclude the trabecular

selection from the cortical region of interest.

First, the region of interest is dilated with a few pixels in 2D to ensure all cortical bone

being included in the region of interest.

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Following the dilation, the trabecular selection is excluded fom the cortical region of

interest by applying a bitwise operation ‘region of interest = region of interest SUB

image’. As such, a donut-like region of interest is created which contains the cortical

bone only.


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In analogy with the trabecular seletion tasklist, the image is now reloaded to allow

saving the region of interest as a binary selection, as well as a new dataset of this

region of interest for further analysis.


Again, the final step is to save a new dataset containing the cortical selection only, as

well as the cortical selection dataset as a region of interest. Therefore, save the

image inside the region of interest, as well as the region of interest dataset as a

binary selection.

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This binary ROI dataset can be loaded as a region of interest in the regions of

interest tab in CTan, selecting bitmap files to be loaded instead of a .roi file, either on

the ‘VOI cort dataset or the newly saved cortical selection dataset.

Original 'VOI cort' dataset

with ROI applied

New cortical selection dataset

with ROI applied

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Part 3: Run the tasklist on multiple datasets using Batch Manager

For high throughput analysis, one can make trabecular and cortical regions

of interest automatically by running the corresponding tasklist to a batch of

bones. To do so, select the ‘Batch manager’ icon in the custom processing tab. The

different steps of the protocol (thresholding, despeckle, bitwise operations, ….) can

be saved in a task list. Therefore each plug-in needs to be added to the task list by

clicking the ‘+’ button (custom processing tab) or ‘add’ button (top level Batch

manager). You can apply the task list to several datasets using batch manager, by

adding the datasets into the bottom part of the batch manager window. Both the task

list and the sample list can be exported if wanted.

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