# Mouse arterial wall imaging and analysis from synchrotron X-ray microtomography

Xiaowen Liang, Aïcha Ben Zemzem, Sébastien Almagro, Jean-Charles Boisson, Luiz Angelo Steffenel, Timm Weitkamp, Laurent Debelle, Nicolas Passat

► To cite this version:

Xiaowen Liang, Aïcha Ben Zemzem, Sébastien Almagro, Jean-Charles Boisson, Luiz Angelo Steffenel, et al.. Mouse arterial wall imaging and analysis from synchrotron X-ray microtomography. SOLEIL Users' Meeting (SUM), Jan 2023, Saint-Aubin, France. . [hal-03946851](https://hal.science/hal-03946851)

HAL Id: hal-03946851

https://hal.science/hal-03946851

Submitted on 20 Jan 2023

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
## Context

<figure><img src="image_2.png" /></figure>

Vascular ageing is characterized by profound modifications of the large elastic arteries (mainly aorta), leading to consequences such as aneurysm formation. Age-related diseases can be precipitated by metabolic disorders, e.g. diabetes or chronic kidney diseases. Vascular diseases involve morphological changes of the arterial extracellular matrix, particularly remodelling of the elastic lamellae found in the wall. Synchrotron X-ray microtomography (µCT) gives access to images with a micrometric resolution. In the context of vascular imaging, this allows the study of structural properties of arterial walls, even for small animals such as the mouse.

<figure><img src="image_3.png" /></figure>

## A methodological pipeline

* Preprocessing: to remove the noise and artifacts.

* Main step 1:

- Segmentation of the lumen

- Computation of the normal field and distance maps with respect to the lumen.

* Main step 2:

- Deep learning-based texture classification.

### 1 : Segmentation of the lumen & computation of the distance maps

* Segmentation of the lumen based on a co-occurrence texture analysis.

<figure><img src="image_4.png" /></figure>

* Finding the contour of the lumen, which is the inner contour of the arterial wall.

<figure><img src="image_5.png" /></figure>

* Computation of a distance map that shows the distance of a pixel to the lumen, in order to calculate a "contour line" at any distance.

<figure><img src="image_6.png" /></figure>

* Calculating the normal vector at any point using "contour lines".

<figure><img src="image_7.png" /></figure>

### 2 : Deep learning-based classification

* Rotating and cutting patches according to the calculated normal vectors.

<figure><img src="image_8.png" /></figure>

* Training a Siamese network to distinguish dissimilar sub-patches.

<figure><img src="image_9.png" /></figure>

* Training on 1457 2D images that generate 47118 patches (20% for validation).

## Results

* Classification at the pixel level by comparison between sub-patches.

<figure><img src="image_10.png" /></figure>

* The Euclidean distance between vector0 and vector2 is smaller than the one between vector0 and vector1, so the corresponding pixel is classified in the region of the surrounding tissues (the other region is that of the elastic lamellae).

* Segmentation on a whole image: extraction of the internal contour by the lumen segmentation + classification of the pixels at the border by the Siamese network.

<figure><img src="image_11.png" /></figure>

Dice score = 0,926
(Ground truth obtained by manual annotation)

## Conclusion

* Chronic cardiovascular diseases are often discovered when irreversible damage has already occurred, early identification of the very first stages of these diseases is therefore extremely important. Synchrotron imaging has shown its power to detect these very localized events with its high resolution. The proposed segmentation pipeline is a first step towards that goal, mixes conventional image processing and deep learning paradigms, and provides promising results.

* The next steps of that work will consist of improving the lumen segmentation step by reducing the parameter space to be handled and of exploring more thoroughly the deep learning part of the process.