# 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

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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. Journée Scientifique autour de l'IA (JSIA), Feb 2023, Nancy, France. . hal-03981316

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Submitted on 4 Apr 2023

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

Distributed under a Creative Commons Attribution 4.0 International License
# MOUSE ARTERIAL WALL IMAGING AND ANALYSIS FROM SYNCHROTRON X-RAY MICROTOMOGRAPHY

Xiaowen Liang<fnref n="1" />,², Aïcha Ben Zemzem², Sébastien Almagro², Jean-Charles Boisson<fnref n="3" />, Luiz-Angelo Steffenel<fnref n="3" />, Timm Weitkamp⁴, Laurent Debelle², Nicolas Passat<fnref n="1" />

## CONTEXT

<figure><img src="image_2.png" /><figcaption>The Structure of an Artery Wall</figcaption></figure>

Cross section of artery (source: Blausen medical)

<figure><img src="image_3.png" /><figcaption>A frame of acquired images</figcaption></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.

## A 2D FRAGMENT ANALYSIS

<figure><img src="image_4.png" /><figcaption>Normal mouse</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Diabetic mouse</figcaption></figure>

Mice are divided into 2 groups (normal and diabetic). A measurement is proposed to quantify their waviness.

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

$$ \rho = \frac{L_{Euc}}{L_{geo}} $$

$L_{geo} = \sum_{0 \le i < k} \|V_{g_{i+1}} - V_{g_i}\|_2$, $k$ : number of pixels, $V_i$ : vertices of curve

$L_{Euc} = \sum_{0 \le j < L_{geo}/\sigma, (j+1)\sigma \le L_{geo}} \|V_{e_{(j+1)\sigma}} - V_{e_{j\sigma}}\|_2$, $\sigma$: sampling rate of $L_{geo}$, $j$ : subsampled indices

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

21 samples, 2000 ~ 3000 fragments for each sample

Mann-Whitney U test :
$p - value = 0.0035 \ll 0.05$

A significant difference between healthy and diabetic mice in terms of elastic lamellae waviness. (Ben Zemzem A., Liang X. et al., *Int. J. Mol. Sci.* **2022**)

## A 2D SEGMENTATION

<figure><img src="image_8.png" /><figcaption>Segmented lumen and normal vectors</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Border of tunica media and adventitia</figcaption></figure>

* The lumen is segmented by a method based on co-occurrence matrix and region growing

* Normal vectors are calculated at each point of the inner contour

* The border between tunica media and adventitia is not clear

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

A Siamese network is used to distinguish 2 types of textures. The similarity of input patches is measured by that of the output descriptors.

* Loss function: $\mathcal{L}(d_{ij}, d_{kl}) = \alpha \cdot \delta_{ik} \cdot \Delta^T \cdot \Delta + \beta \cdot (1 - \delta_{ik}) \cdot [\max\{0, m_p - (\Delta^T \cdot \Delta)^{1/2}\}]^2$, $\Delta = d_{ij} - d_{kl}$, $\alpha = 2$, $\beta = 1$, $m_p = 2$

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

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

Training samples are obtained from patches oriented with the help of normal vectors. Each time a pair of sub-patches is an input, with "green-green" labeled as 1, "green-red" labeled as 0.

* Dataset: 47118 image patches of size 64² generated by 1457 2D slices

<figure><img src="image_14.png" /><figcaption>Prediction on a patch</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Prediction on an entire image</figcaption></figure>

## A 3D SEGMENTATION

<figure><img src="image_16.png" /><figcaption>3D image with agarose</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Extracted elastic lamellae</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Extracted lumen</figcaption></figure>

* The elastic lamellae and the lumen are extracted by moving spheres

* For elastic lamellae extraction, use a single sphere to count the number of inner pixels

* For lumen extraction, use 3 concentric spheres to count the number of pixels between each 2 neighboring spheres

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

* Straighten a stack of 2D images

* Extract the upper space by region growing

* Reject regions that are erroneously spread (green) by comparing their "weights" and their connections to the upper space

<figure><img src="image_21.png" /></figure>

* With the upper space extracted, the first elastic lamella is the boundary between this space and the rest of the background

<figure><img src="image_22.png" /><figcaption>The first elastic lamella in its initial shape</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>2D frame with the first lamella marked</figcaption></figure>

## CURRENT WORK

* Extend the 2D segmentation approach to 3D to distinguish tunica media and adventitia in 3D

* With the segmented region of elastic lamellae in 3D, segment the other 4 layers of them

* Extend the 2D waviness analysis to 3D and apply it to 5 elastic lamellae individually

<footnote n="1">¹Université de Reims Champagne-Ardenne, CReSTIC EA 3804, 51100 Reims, France, ²Université de Reims Champagne-Ardenne, CNRS, MEDyC UMR 7369, 51100 Reims, France,</footnote>

<footnote n="3">³Université de Reims Champagne-Ardenne, LICIS / LRC CEA DIGIT, 51100 Reims, France, ⁴Synchrotron SOLEIL, 91192 Gif-sur-Yvette, France</footnote>