# From Specimen to Digital Twin: High-Throughput Pipeline for Synchrotron Imaging of Museum Collections

Camille Berruyer, Sofiane Terzi, Awen Autret, Duy Nguyen, Limamou Gueye, Barbara Fayard, Jonathan Blettery, Pauline Provini

## ► To cite this version:

Camille Berruyer, Sofiane Terzi, Awen Autret, Duy Nguyen, Limamou Gueye, et al. From Specimen to Digital Twin: High-Throughput Pipeline for Synchrotron Imaging of Museum Collections. TECHNART 2025 - International Conference on Analytical Techniques for Heritage Studies and Conservation., May 2025, Perugia, Italy. mnhn-05072235

HAL Id: mnhn-05072235

https://mnhn.hal.science/mnhn-05072235v1

Submitted on 19 May 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# From Specimen to Digital Twin: High-Throughput Pipeline for Synchrotron Imaging of Museum Collections

Camille Berruyer¹, Sofiane Terzi¹, Awen Autret¹, Duy Nguyen¹, Limamou Gueye¹, Barbara Fayard¹, Jonathan Blettery², Pauline Provini²

¹NOVITOM, Grenoble, France

²Museum National d'Histoire Naturelle (MNHN), Paris, France

camille.berruyer@novitom.com

More and more museums and institutions are recognising the importance of digitising their collections and the immense scientific potential that lies in accessing the internal structures of objects housed within their walls. One of the main challenges associated with scanning vast volumes of data is ensuring that the resulting information is easily accessible to both researchers and the public. Thus, developing an effective methodology for data processing and management becomes central.

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

2

## Al segmentation

A 3D U-Net architecture is trained for volumetric segmentation using annotated data generated with Novitom's manual labeling software. Training is performed on sub-volumes of size 256×256×256 to optimize memory usage and model convergence. During inference, a sliding window approach with overlapping tiles is employed to process larger volumes and ensure seamless predictions.

1

## Microtomographical scans and reconstruction

After the sample reception, we schedule and perform the synchrotron beam based microtomography. We reconstruct the scans thanks to our algorithmic tools.

3

## Generation mesh pipeline

* Dilation: Expands the binary segmented volume produced by AI to fill small gaps and holes.

* Mesh generation: Converts the dilated volume into a 3D mesh using marching cubes.

* Mesh decimation: Reduces the mesh complexity while preserving its overall shape.

<figure><img src="image_3.png" /><figcaption>005-C05 specimen
Non-decimated mesh (5900 Mo)</figcaption></figure>

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

<figure><img src="image_5.png" /><figcaption>005-C05 specimen
decimated mesh (107 Mo)</figcaption></figure>

5

## Metadata

* Standardizing data names and ensuring they correspond to the inventory numbers provided by the museum.

* XML file generation giving all the scan parameters.

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

4

## Display parameters tuning

Tuning of the display parameters to match the museum wishes.

Bonus
3D rendering

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

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

The result is a comprehensive digital twin of each specimen, preserving intricate morphological details while ensuring broader accessibility via dedicated web platforms. This digital archiving not only facilitates the dissemination and study of scanned artefacts but also opens opportunities for discovering previously inaccessible or hidden features.

## Synchrotron scan parameters

<table><tr><td>Pixel size</td><td>1.4 to 6 µm</td></tr><tr><td>X ray source</td><td>BM18 BM05 @ESRF</td></tr><tr><td>Energy</td><td>75 to 95 keV</td></tr><tr><td>Distance</td><td>300 to 1400 mm</td></tr></table>

## Practical example

Project e-COL+ (ANR-21-ESRE-0053) from the National Museum of Natural History (MNHN) in Paris

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

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