# Deep learning application in Volume Electron Microscopy (vEM) image analysis

Manuel Majrouh, Perrine Bomme, Clarisse Leseigneur, Carmen Buchrieser, Adeline Mallet

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Manuel Majrouh, Perrine Bomme, Clarisse Leseigneur, Carmen Buchrieser, Adeline Mallet. Deep learning application in Volume Electron Microscopy (vEM) image analysis. Journée départementale du BCI (Biologie Cellulaire et Infection), May 2025, Paris, France. pasteur-05421768

HAL Id: pasteur-05421768

https://pasteur.hal.science/pasteur-05421768v1

Submitted on 17 Dec 2025

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# Deep learning application in Volume Electron Microscopy (vEM) image analysis

Majrouh M<fnref n="1" />*, Bomme P<fnref n="1" />, Leseigneur C², Buchrieser C², Mallet A<fnref n="1" />

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FRANCE-BIOIMAGING

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CORE FOR LIFE

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INSTITUT
PASTEUR

Ultrastructural Bioimaging
Core facility

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https://www.pasteur.eu/ppms/login/?pf=5

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## Introduction

Volume Electron Microscopy (vEM) plays a pivotal role in the three-dimensional study of host-pathogen interactions. Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) generates hundreds to thousands of images at nanometric resolution (~10 nm voxel size), allowing detailed analysis of cellular ultrastructure. To reconstruct 3D volumes from these datasets, a series of **image processing steps** is required:

* **Image alignment** to correct for drift and distortion across slices,

* **Denoising and contrast enhancement** to improve the visibility of fine structures,

* **Segmentation**, often assisted by deep learning, to identify and isolate features of interest such as membranes, organelles, or pathogens,

* And finally, **3D rendering and quantification** to interpret and analyze spatial relationships within the volume.

In this presentation, we will explore the **complete image analysis workflow** that enables visualization of host-pathogen interactions, and how it can be **accelerated using deep learning tools**. A key question we will address is: **Can this workflow be adapted across different vEM datasets to tackle a variety of biological questions in the context of host-pathogen interactions?**

## General image analysis workflow and deep learning implementation

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## Application on Legionella pneumophila study in FIB-SEM

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**Infectious site inside the zebrafish yolk (FIB-SEM).**

A. Schematic diagram of FIB-SEM acquisition, showing 4 images of the stack separated by 30 nm each. The electron beam scans the sample for acquisition, while the ion beam mills its surface. After image alignment, ROIs can be tracked in 3 dimensions.

B. Ultrastructure of the infection site. We can distinguish in these 2D slices different bacteria conformations inside their vacuole. Lp: Legionella pneumophila, black arrows: Legionella Containing Vacuole (LCV), Mito: mitochondria, F: fatty substance.

C. Resulting 3D model after the AI-assisted segmentation, with a zoom on the infection site in 3 dimensions. The deep learning training took 2 hours and the manual correction 8 hours.

In conclusion, we propose how to use a user-friendly image analysis pipeline accelerated by deep learning. It enables us to answer different biological questions depending on the type of Volume EM acquisition and is usable on tomography data as well (in room temperature or cryo condition). These results were produced with Amira® software from ThermoFisher: it features all the image analysis steps, as well as an AI-assisted segmentation module. However, there are other software that can do vEM image analysis with neural network training (DragonFly 3D World® and Arivis Pro® for example). Moreover, as the segmentation produced is semantic, we need to add another image analysis step to separate the instances of a biological object and thus produce quantifications. Deep learning can also be used to filter image stacks before the segmentation.

<footnote n="1">¹ Institut Pasteur, Université Paris Cité, Université de Paris Sorbonne, Ultrastructural Bioimaging Unit, Paris, France;
² Biology of Intracellular Bacteria unit (BBI), Institut Pasteur, CNRS UMR 3525, 75015 Paris, France</footnote>